{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = '..\\data\\种类和分解率和生长速率.xlsx'\n",
    "dataF = pd.read_excel(filename, sheet_name=None)\n",
    "sheets = list(dataF.keys())\n",
    "Decomposition_Rate = np.array(dataF[sheets[2]]).T\n",
    "extension_Rate = np.array(dataF[sheets[3]]).T\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['figure.figsize'] = (3.0,3.0)\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "for i in range(3):\n",
    "    ind = (i) * 3 + 1\n",
    "    y = Decomposition_Rate[ind][Decomposition_Rate[ind].size-34:Decomposition_Rate[ind].size - 1]\n",
    "    x = extension_Rate[ind][extension_Rate[ind].size-34:extension_Rate[ind].size - 1]\n",
    "    f = np.polyfit(x.tolist(),y.tolist(),1)\n",
    "    p1 = np.poly1d(f)\n",
    "    yval = p1(x)\n",
    "    plt.xlabel(\"Extension Rate\")\n",
    "    plt.ylabel(\"Decomposition Rate\")\n",
    "    plt.plot(x,yval)\n",
    "    plt.scatter(x,y, s = 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1daddc9b070>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABVAAAAI3CAYAAAB9KHAkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAB7CAAAewgFu0HU+AACDUUlEQVR4nOzdeXxdZZ348c+3FCotUI1iAUNEpNUiVVHcquOoqbhvY90XXJCpjmNcRx13R8d9ic5oVVxwGWecum/jSPwJ1aqAaxzBRBFCVFpKJdpWCiXf3x/nBNKSm6W5OXf7vF+v8zr3Pue55/mml5Kn3/MskZlIkiRJkiRJkm5sUaMDkCRJkiRJkqRmZQJVkiRJkiRJkmowgSpJkiRJkiRJNZhAlSRJkiRJkqQaTKBKkiRJkiRJUg0mUCVJkiRJkiSpBhOokiRJkiRJklSDCVRJkiRJkiRJqsEEqiRJkiRJkiTVYAJVkiRJkiRJkmowgSpJkiRJkiRJNZhAlSRJkiRJkqQaTKBKkiRJkiRJUg0mUCVJkiRJkiSpBhOokiRJkiRJklSDCVRJkiRJkiRJqmFxowNQISKWAGvKt1cA1zUwHEmSpANxEHBk+XowM/c0MhjNnX1SSZLU4hakP2oCtXmsAc5vdBCSJEl1cjfggkYHoTmzTypJktpF3fqjTuGXJEmSJEmSpBocgdo8rph4cd5553H00Uc3MhZJkqQ5++Mf/8jd7373ibdXTFdXTcs+qSRJalkL1R81gdo8rl9f6uijj6a7u7uRsUiSJM2Xa2e2JvukkiSpXdStP+oUfkmSJEmSJEmqwQSqJEmSJEmSJNVgAlWSJEmSJEmSajCBKkmSJEmSJEk1mECVJEmSJEmSpBpMoEqSJEmSJElSDSZQJUmSJEmSJKkGE6iSJEmSJEmSVIMJVEmSJEmSJEmqwQSqJEmSJEmSJNVgAlWSJEmSJEmSajCBKkmSJEmSJEk1mECVJEmSJEmSpBpMoEqSJEmSJElSDSZQJUmSJEmSJKmGxY0OQJIkqWqDo2P0DwwxsmM3PV1L6etdxZru5Y0OS5IkSR3C/mhrMYEqSZI6yuDoGOs3bmHP3nEAhrbuZPPwdjZtWGunVZIkSQvO/mjrcQq/JEnqKP0DQ9d3Vifs2TtO/8BwgyKSJElSJ7E/2npMoEqSpI4ysmN3jfJdFUciSZKkTmR/tPWYQJUkSR2lp2tpjfJlFUciSZKkTmR/tPWYQJUkSR2lr3cVSxbv2wVasngRfb0rGxSRJEmSOon90dbjJlKSJKmjrOlezqYNa+kfGGZkxy56upbR17vSBfslSZJUCfujrccEqiRJ6jhrupdz5mmnNDoMSZIkdSj7o62l7afwR8QREfHWiPhdRIxHxHUR8auIeH1ELGl0fJIkSWod9i0lSZI6T1snUCPicOD7wMuB44AR4EpgNfA64AcRceh+nzkpInKG49UV/yiSJElqsAPpW86jrYfPok/61Hq0JUmSpOm1dQIV6AdOAn4GrM7M44AVwN8BO4GTgX/e7zP3LM/XAXtqHHsXOG5JkiQ1nwPpWx6oiT7pXmr3Sa+rU1uSJEmaRtsmUCNiDfBMYAx4aGZeBJCFLwL/Ulbd/8n9PcrzKzLzJjWOt1byQ0iSJKkpzKNveaAm+qRPmqZP+tk6tSVJkqRptG0CFXgMxRP792fmH6e4/rPyfMx+5ROd1QsWKC5JkiS1ngPtW85ZRARwt/KtfVJJkqQGW9zoABZKZr4xIt5C7SRxd3m+fKKgXNfqDhTToeysSpIkCTiwvuU8nAgsB7Zm5iV1uJ8kSdK8DY6O0T8wxMiO3fR0LaWvdxVrupc3OqxKtG0CFSAzr52qPCIOBp5Xvv3ypEv3oOgUn5+ZOxc4PEmSJLWQA+hbHqh7ledz63AvSZKkeRscHWP9xi3s2TsOwNDWnWwe3s6mDWs7IonazlP4pxQRJwJfA+4KXAq8YdLlic7qdRHxpYj4fURcHRGXRcSnI+Je+99PkiRJnWuGvuWBmuhzHhER34qIy8s+6cUR8eGyTUmSpMr0DwxdnzydsGfvOP0Dww2KqFptPQJ1soh4OvA64Piy6AJgfWZeOana2vJ8T+BqYBD4NcVuq08BnhwRr8zMtx1A+90zVDlqrveUJElSY8yyb3mgJvqkDwL+AvwSuBa4E/Ac4BkR8czM/Mxcb2yfVJIkHYiRHbtrlO+qOJLG6JgEKsXPetik9yuAO1KMFCAiFnHD0/6zgJdl5hXltSXAq4DXAG+NiHMy84dzbP+yecQuSZKk5jJt3/JARcQtgNuVb98GvDkz/1JeOxx4N3A68NGI2JyZI3Nswj6pJEmas56upQxtvfFqlz1dyxoQTfU6Zgp/Zn4sM1cAdwe+ChwLfDEiHl5WWQQ8EngU8MyJ5Gn52T2Z+VrgP8uil1YXuSRJkprNLPqWB2o3sA54UGa+YiJ5Wrb5F+AM4AfAEuAF82xLkiRpVvp6V7Fk8b5pxCWLF9HXu7JBEVUrMrPRMVQuIoJigf9HAH8AejLzull8rhc4m2Iq1fKcwx/eLKdLnQ9w2WWX0d09U3VJkqTmMjo6yrHHHjvx9tjMHG1kPFU50L7lPNp7NnAm8H+ZedIcP2ufVJIkHZDB0TH6B4YZ2bGLnq5l9PWubLoNpBaqP9pJU/ivl5kZEe+g6OQeA6ymWFtqJhN/6IcDRwLb5tDmtF9Y0e+WJElSq5lH3/JATfQrj5+21hTsk0qSpAO1pns5Z552SqPDaIi2ncIfEYsj4sSIOKhGld9Men1E+ZmIiEOmue2Rk1533tBdSZKkDnUgfct5tHVQREw30GGiT2p/VJIkqQJtm0AFfgz8H/CYGtd7Jr3eGhEbgK3AS6a55/3L8w5g+7wjlCRJUquYU9/yQBuJiDcAVwKPm6baRJ906EDbkSRJ0uy1cwL1m+X5rRFx0ymu/2N5viQzf0uRFD0SeE5ELNm/ckR0Ac8v335pLuufSpIkqeXNtW95oHYBy7mh37mPiDgBeHL59gvzaEeSJEmz1M4J1PcBY8BtgXMi4gERcWhELC+f7D+lrPfa8vxF4GLgNsDHIuKwiRuVHdXvALcs7/mvFf0MkiRJag5z7VseqI8CVwFrI+Idkx/sR8RdKTY0vQlwGfD+ebYlSZKkWWjbBGpm/gF4PMVT/DsCA+Xrqyg6tuPAazLzU2X9a4EnldefDFweET+OiF8CvwbuRLFg/0PnOapAkiRJLWaufcsJEXF1ebx6lu1cCTwN2AO8lKJPen5E/Bq4ALg18Cvg1My8qg4/miRJkmbQtglUgMz8X4oO7kcontJfB1wBbALunZlv2q/+ecAaYCPFCIM7AiuAc4EXAqszc0tV8UuSJKl5zLVvWVpSHtNtCrV/O18DTgY+A1xL8SB/OfA/wLOBkzPzogP/SSRJkjQXs+7ItarMvBg4Yw71R4HnlockSZJ0vQPoW8YBtnMh8NQD+awkSZLqq61HoEqSJEmSJEnSfJhAlSRJkiRJkqQaTKBKkiRJkiRJUg0mUCVJkiRJkiSpBhOokiRJkiRJklSDCVRJkiRJkiRJqmFxowOQJEnSzAZHx+gfGGJkx256upbS17uKNd3LGx2WJEmS1PZMoEqSJDW5wdEx1m/cwp694wAMbd3J5uHtbNqw1iSqJEmStMCcwi9JktTk+geGrk+eTtizd5z+geEGRSRJkiR1DhOokiRJTW5kx+4a5bsqjkSSJEnqPCZQJUmSmlxP19Ia5csqjkSSJEnqPCZQJUmSmlxf7yqWLN6327Zk8SL6elc2KCJJkiSpc7iJlCRJUpNb072cTRvW0j8wzMiOXfR0LaOvd6UbSEmSJEkVMIEqSZLUAtZ0L+fM005pdBiSJElSx3EKvyRJkiRJkiTVYAJVkiRJkiRJkmowgSpJkiRJkiRJNZhAlSRJkiRJkqQaTKBKkiRJkiRJUg0mUCVJkiRJkiSpBhOokiRJkiRJklSDCVRJkiRJkiRJqsEEqiRJkiRJkiTVYAJVkiRJkiRJkmowgSpJkiRJkiRJNZhAlSRJkiRJkqQaFjc6AEmSJEmSpFYxODpG/8AQIzt209O1lL7eVazpXt7osCQtIBOokiRJkiRJszA4Osb6jVvYs3ccgKGtO9k8vJ1NG9aaRJXamFP4JUmSJEmSZqF/YOj65OmEPXvH6R8YblBEkqpgAlWSJEmSJGkWRnbsrlG+q+JIJFXJBKokSZIkSdIs9HQtrVG+rOJIJFXJBKokSZIkSdIs9PWuYsnifVMpSxYvoq93ZYMiklQFN5GSJEmSJEmahTXdy9m0YS39A8OM7NhFT9cy+npXuoGU1OZMoEqSJEmSJM3Smu7lnHnaKY0OQ1KFnMIvSZIkSZIkSTU4AlWSJEktIyIOAY4DllP0Zf8CjGbmVQ0MS5IkSW3MBKokSZKaWkTcBXgqcCpwO6aYRRUR24DvAF8AvpSZ11UapCRJktqWCVRJkiQ1pYhYC7wVuDcQwK+BTwFDwFXAtcDhwG2AOwHrgScCf4yINwJnZuZ49ZFLkiSpnZhAlSRJUlOJiKXAO4G/B/YA7wM+kJnDM3zucOBpwEuBDwLPjIinz/Q5SZKkZjY4Okb/wBAjO3bT07WUvt5VrOle3uiwOoqbSEmSJKlpRMTxwA+BDcDXgFWZ+aLZJEEz8y+Z+QGKaf6vBe4CnB8RD1/ImCVJkhbK4OgY6zdu4ewLtzG0dSdnX7iN9Ru3MDg61ujQOooJVEmSJDWFcq3THwC3B/4xMx+VmaNzvU9mXpuZbwbuA+wGvhgRz6hrsJIkSRXoHxhiz959VyTas3ec/gEn2FTJBKokSZKaxYMp1jR9RGb++3xvlpnnA/cERoDHzvd+kiRJVRvZsbtG+a6KI+lsJlAlSZLUFDLzX4E7ZOa36njPEYqRqCZQJUlSy+npWlqjfFnFkXS2tk+gRsQREfHWiPhdRIxHxHUR8auIeH1ELGl0fJIkSbpBZv5uAe75x8y8pt73lSRJWmh9vatYsnjf9N2SxYvo613ZoIg6U1snUMudWL8PvBw4jmL61pXAauB1wA8i4tCpPlcmXX8bEXsi4pKIeIMJV0mSpOYREQdHxMkR0Vu+PzIi7tfYqCRJkupnTfdyNm1Yy7rVK1i14jDWrV7Bpg1rWdO9vNGhdZTFjQ5ggfUDJwE/A56UmRdFRACPBj4JnAz8M/CaiQ9ExM2Ac4A1ZVECt6bYyfU+EfGgzNxb1Q8gSZKkfUXEcuAdwFOAmwBXA8uA44GBiPgl8NjM/E3jopQkSaqPNd3LOfO0UxodRkdr2xGoEbEGeCYwBjw0My8CyMIXgX8pqz51v4/+F0Xy9K/Asyg65ccC3wYeALxk4aOXJEnSVCJiKbAZeDZwKBDlAcWD76B4gP6/5WwkSZIkaV7aNoEKPAbYC7w/M/84xfWfledjJgoi4mHAA8u3z8nMj2fmNZk5CjwO+BPw+oi4+cKFLUmSpGm8iCJBegXwjMkXMvM84H7AZRQziF5ccWySJElqQ22bQM3MNwJLgTfVqNJdni+fVLahPA9m5mf2u98YcCbFiNSH1jFUSZIkzd7jKUaaPjszP7n/xcw8FzidYiTq+opjkyRJUhtq2wQqQGZem5l79i+PiIOB55Vvvzzp0t+U58/VuOU3yvND6hOhJEmS5uiE8nz2NHXOoUiy3nbhw5EkSVK7a/dNpG4kIk4E3gPcFbgUeENZfiQwsYXZD2p8/Bfl+fYH0G73DFWOmus9JUmSOtB1FMnR6Tb1XEoxAtWNPyVJkjRvHZNAjYinA6+j2J0V4AJgfWZeWb6fvK7p0FT3yMwdEbEXOO4AQrjsAD4jSZKkfQ0BJwN3Bn5co866SXUlSZKkeWnrKfz7WQwcNun9CuCOk94vmfT6T9Pc5yrgZhGxZJo6kiRJWhifpxhd+raIOGT/ixFxLPBOilGqn684NkmSJLWhjkmgZubHMnMFcHfgq8CxwBcj4uFllesmVd89za2uKc+HzjGEY2c47jbH+0mSJHWi9wMXA/cHLirLFkfE2yLiC2XZrYFLyrqSJEnSvHTMFP4JmXl+RDyKYvOoRwAfioge4K9llb2ZOT7NLSauLaUYjTrbdkenux4Rs72VJElSx8rMnRHxUIrNPSeWZjoIeCnFyFQoEqwPy8ydDQhRkqRKDY6O0T8wxMiO3fR0LaWvdxVrupfP/EFJs9ZxCVSAzMyIeAdFAvUYYDXwh/Ly4ohYkZlba3z8ZuXZjKckSVIDZOZQRJwEnA48hBvWp7+EIrH60cy8ujHRSZJUncHRMdZv3MKevcVYr6GtO9k8vJ1NG9aaRJXqqG2n8EfE4og4MSIOqlHlN5NeH5GZO4Bd5fvb1LjnMmBZ+dYRDZIkSQ2SmVdn5r9l5sMy8w7l8bDM/HeTp5KkTtE/MHR98nTCnr3j9A8MNygiqT21bQKVYlfW/wMeU+N6z6TXE6NNJ3ZyPaXGZ+5Znv+cmWPzC0+SJEmSJOnAjeyYeguXkR27piyXdGDaOYH6zfL81oi46RTX/7E8X5KZvy1ff7s8P7HGPR9Ynn8y//AkSZK0ECLiJhFxREQc3OhYJElaSD1dS2uUL5uyXNKBqTSBGhHLIuJpEdEfER8vy06MiE9ExN3r3Nz7gDHgtsA5EfGAiDg0IpZHxBuAp5T1XjvpM5+i2CTq3hHxiP1iPxJ4Tvn2a3WOVZIkSbMQET0RcewM1f4B+BPw4QpCkiSpYfp6V7Fk8b6pnSWLF9HXu7JBEUntqbIEarlb6qXAJ4DnA08oLx0OPB34fkRsqFd7mfkH4PEU65reERgoX19FkTQdB16TmZ+a9JlLgf8s3/5HRDylTLreFfgW0FV+/uP1ilOSJElzcglw0Qx1Pk+x4ecDFjwaSZIaaE33cjZtWMu61StYteIw1q1e4QZS0gJYXEUjEXEisAm4CcXmS4dNunwFxYZOJwDvi4gfZeZP69FuZv5vRNwReAXwYOBoitEI5wDvyswfTvGx5wMnAncGPr3ftXHgjHLDKUmSJDVGTHcxMy+JiASOqigeSZIaZk33cs48rdZWLpLqoaoRqK+gSJ5+jf06spl5MXB74L8pErovqWfDmXlxZp6RmT2ZeXBm3jIzH1cjeUpm/gm4D/BuihGrEy4CHpqZ/13P+CRJklRfEbEYSOC6RsciSZKk1ldVAvUBFJ3Yl2XmjbaIy8xxiiQrwP0riqmmzNyVmS8BVgCnAKsyc3VmfqvBoUmSJGlmf0fRz/11owORJElS66tkCj9wJDCemdN1Yi+hSLLeopKIZiEzdwE/bnQckiRJnSgiTgNOm+LSIRHxnRofWw7ciaJf+ZGFik2SJEmdo6oE6lXALSLisMzcWaNOD8V6VldVFJMkSZKa23HA/aYoX1SjfLIPZeYH6hwPEXEE8M8UG6LemiJR+2vgc8BbMnNPvduUJElSY1WVQD0PeCjwKOAzNepsmFRXkiRJ+hLFLKUJAXwMuBY4o8Zn/gr8JDN/U+9gIuJw4PvASWXRpcBSYDXwOuCREXHvzPxrndpbAbweeDhwS+B3wEbgfeUSWJIkSapAVQnUDwEPA94bEVdMvhARNwVOp9g8Ksu6kiRJ6nCZ+XPg55PLIuJjwHWZeVYDQuqnSJ7+DHhSZl4UEQE8GvgkcDLF6NTXzLehiLg18D2guyxK4HbAe8p2plraQJIkSQugkk2kMvNrFJ3KmwPfpOgALomIrcB24G0UydzPlHUlSZKkphERa4BnAmPAQzPzIoAsfBH4l7LqU+vQ1sHANyiSp1dSJGgPBm5Pkbx9ekQ8Yb7tSJIkaXYqSaCWngW8BdhDMf0qKDaXWgRcA7wVeEaF8UiSJEmz9RhgL/D+zPzjFNd/Vp6PqUNbZwAnUgw6eExmfjkzrys3ZH1sGcd7I+KgOrQlSZKkGVQ1hZ9ynaZXRcS7gQdQbAoAxbpW38nMK6uKRZIkSS3rNhSJxUpl5hsj4i3UHoAwMdX+8jo0N7E3wNcyc/N+cVwcEV8EHgesBTbv/2FJkiTVV2UJ1AllovS/q25XkiRJrS8zL21g29dOVV5OuX9e+fbL82kjIm7GDZtUfa5GtW9QJFAfgglUSVILGhwdo39giJEdu+npWkpf7yrWdC9vdFhSTZUnUCVJkqSFViY1N2Tm+xe4nRMpNna6K3Ap8IZ53vKESa9/UKPOL8rz7ed684jonqHKUXO9pyRJczE4Osb6jVvYs3ccgKGtO9k8vJ1NG9aaRFXTapoEakTcD7gjcF5m/rCx0UiSJKlZRcRxwKuAewLLpqoCHE3R112QBGpEPB14HXB8WXQBsL4Oy1LdvDxfS7HU1VS2lefjDuD+lx3AZyRJqpv+gaHrk6cT9uwdp39gmDNPO6VBUUnTqySBGhGvBfZm5r9OU2058F7gO8C6KuKSJElSa4mIHuA8ikRjzFD96gUMZTFw2KT3KygGA8x3iYEl5fnPmXldjTp/Ks9Hz7MtSZIqN7Jjd43yXRVHIs1erUXw6+31wKtnqPPd8nzygkYiSZKkVvZq4BbAIMU6oD+kGK35QOD+wDfLel8EbrVQQWTmxzJzBXB34KvAscAXI+Lh87z1RNJ06n9dFq4pz4cewP2PneG42wHcU5KkWevpWlqjfKpJJVJzqCqBOqPMHKPoMB7R6FgkSZLUtNYBCTwhMz8P9FOMBr06M88BHgH8FHg0N2zGtGAy83zgURRJ1IOAD0XEQfO45V/L8zXT1JmY9zj1v0CnkZmj0x3A5XO9pyRJc9HXu4oli/dNRy1ZvIi+3pUNikiaWdMkUMsdRw8CxhodiyRJkprWMcB1mfnr8v33Kaby3xUgM8eBfynLXlhFQJmZwDsmxbd6HrebmJ5/zDR1blaeZ1rCQJKkprOmezmbNqxl3eoVrFpxGOtWr3ADKTW9uq+BGhFHADed+lIcy9QdveXAP1OMJvh+vWOSJElS2/gzcPOIOCQzr8nM0Yj4M/vuSH92eb53vRqNiMXAKuDXNdYm/c2k1/OZUTVSng+NiKMyc6oRoSvK8855tCNJUsOs6V7uhlFqKQuxidSLgNdOUX4ItXcShSKxeg3FiAFJkiRpKhdTbCD1UOBLZdn/se86+nsoHszXc2moH1NsEvU4YNMU13smvd56oI1k5vaIGCnvdwrwtSmq3bM8j0xxTZKkuhocHaN/YIiRHbvp6VpKX+8qR4uq4yzEFP6Y4qhVPnHsoRh5empmXrAAMUmSJKk9fIai/3hWRDyzLPsBcLeIeFD5/lllncvq2O7E5lRvjYibTnH9H8vzJZn523m29e3y/MQa1x9Ynn8yz3YkSZrW4OgY6zdu4ewLtzG0dSdnX7iN9Ru3MDjq6ovqLHVPoGbm6zNz0eSjvHT1/uWTjqWZ+TeZeW6945EkSVJb+QhwIXA4xYZRAJ+iSJh+IyJ2AB+gGIE61ejNA/U+irX6bwucExEPiIhDI2J5RLwBeEpZb6qZWHP1ifL8hIi4y+QLEXF74DHl23r+fJIk3Uj/wBB79o7vU7Zn7zj9A8MNikhqjKbZREqSJEmaSWZeDdwH+DBwRVn2c+BNFEnUm5bnPwBvr2O7fwAeD+yimMo/UL6+iiJpOg68JjM/NflzEXF1ebx6Dm19j2J21mLg6xHx0IhYEhH3pxgJewjwW+Ar8/7BJEmaxsiO3TXKd1UcidRYC7EGqiRJkrRgMvNPwIb9yl4XEd8E7ksxUvRzZb16tvu/EXFH4BXAg4GjgT8B5wDvyswfTvGxJeV5rv3upwDnUqyF+vX9rl0NnJaZ187xnpIkzUlP11KGtt54z8KermUNiEZqnKoSqOdSrHMqSZIkLYgygTlVErOebVwMnDGH+jFzrSk/d2lEnEIxivbJFKNOAc4Dnp+Z5x/IfSVJmou+3lVsHt6+zzT+JYsX0de7soFRSdWrJIGamferoh1JkiSpXWTmFcAzI+KFwErgisy8tLFRSZI6yZru5WzasJb+gWFGduyip2sZfb0rWdO9vNGhSZVquin8ERGZmY2OQ5IkSWoGmTkGXNDoOCRJnWlN93LOPO2URochNVSlCdSIWAKcBEy1WEYAtwGeB9y9yrgkSZLUGiLibyj6jT/MzGsaHY8kSZLaX2UJ1Ih4PvBm4LCq2pQkSVLb+QJwM2AFcGWDY5EkSVIHWFRFIxHxCOB9wOHAX8riBEbKI8rj98CmKmKSJElSS7qUot/o9r+SJEmqRCUJVOAfKRKm7wS6gH8ry0/JzNsATwXGgT8CT6soJkmSJLWeL5XnBzQyCEmSJHWOqhKodynPb8rMcYqpV4uA+wBk5n8AZwKnABsqikmSJEmt58PADuB1EXHTBsciSZKkDlBVAvUw4LrM/HP5/sfl+U6T6nyQYjrWUyqKSZIkSS0mM7dR9BePBL4bESc0OCRJkiS1uao2kfo9cFxErMjMrZm5MyIuA243qc4vKab5r64oJkmSJLWYiHh6+fJDwAuBX0TEh4CfTlU/Mz9ZUWiSJElqU1UlUH8JHAe8mmI9VICfA38TEZGZCdycYgTq3opikiRJUuv5BMVDdyj6jjcBXjBNfROokiRJTWBwdIz+gSFGduymp2spfb2rWNO9vNFhzUpVCdR3A48AnhcRh2fmM4CzgYcDn4qIzwF/X9a9qKKYJEktqJV/6Uqqi3O5IYEqSZKkFjA4Osb6jVvYs3ccgKGtO9k8vJ1NG9a2xL/nKkmgZuY5EfE24OXAEWXxJ4BXAU8qDyg6wx+pIiZJUutp9V+6kuYvM+/X6BgkSZI0N/0DQ9f/O27Cnr3j9A8Mc+ZppzQoqtmrahMpMvOVwL2A/yrf/5liVOowxfSr64APZebHq4pJktRapvulK0mSJElqTiM7dtco31VxJAemqin8AGTmj4AfTXp/HnD7iLglsCszW+NPTZLUEK3+S1eSJEmSOlFP11KGtu6conxZA6KZu0oTqLVk5rZGxyBJan6t/ktXkiRJkqbTrns+9PWuYvPw9n1mFC5ZvIi+3pUNjGr2miKBKknSbLT6L11JkiRJqqWd93xY072cTRvW0j8wzMiOXfR0LaOvd2XL/FwmUCVJLaPVf+lKkiRJUi2tvtHSTNZ0L2/Zn6OSBGpEbAQOBl6SmVdV0aYkqT218i9dSZIkSarFPR+a16KK2nkI8AwgKmpPkiRJkiRJahk9XUtrlLvnQ6NVlUD9RXnuqai9miJiUUS4dIEkSZIkSZKaRl/vKpYs3jdV554PzaGqBOpnKEafPrWi9vYREfeIiC9FxJXAdcC1EfHbiHhNRNxkivonRUTOcLy6+p9EkiRJkiRJ7Whiz4d1q1ewasVhrFu9oi02kGoHVY3E/C/gOUBfRAxk5v9U1C4RcRrwUeAgYDfwQ+CWwPHAG4EHR8QDMnPPpI/dszxfB+ytceta5ZIkSZIkSdKcuedDc6pkBGpmJvB44HvAlyPi1RGxZKHbjYg7AR+mSJ6+C1iRmfcCTgCeX1ZbC7xsv4/eozy/IjNvUuN460LHL0mSJEmSJKmxKhmBGhHfmdTeYuANwPMi4qIpqmdm9tap6bcDhwBvz8yXT24A+PeIeCDwKOBJwJsmfW4igXpBneKQJEmSJEmS1IKqmsJ/vynKjiqP/WU9GoyIZRRT9s+nmKo/lfMpEqjHTPrc4cAdKKbvm0CVJEmSJEmSOlhVCdRnVtTO9TJzF/CYGapNJE53TSq7B8XSBudn5s6FiE2SJEmSJElSa6gkgZqZZ1XRzlxExEEUo08BBiZduld5vi4ivgTcDbg5cAVwDvDvmfmDquKUJEmSJEmS1DhVjUBtRs8CblW+fu+k8rXl+Z7A1cAg8GvgJOApwJMj4pWZ+ba5NBYR3TNUmWo5A0mSJEmSJEkN1JEJ1Ig4Gnhr+fbjmfnTsnwRN4xAPQt4WWZeUV5bArwKeA3w1og4JzN/OIdmL6tL8JIkSZIkSZIq03EJ1DJJ+kmgC7gUePGky4uARwI3Bb6amddvaJWZe4DXRsRK4InAS4H1FYUtSZKkUkSsAPqANcCyaapmZvZWE5UkSZLaVcclUIF/AdYBe4AnZOZVExcycy9w7gyfP5MigXpqRMTkJOsMjp3h+lHA+bO8lyRJUkeKiDsD3wGWAzFD9dn20yRJkqSaOiqBGhFPAP65fPvczPzRAdxmtDwfDhwJbJvNhzJzdLrrETP1/yVJkkSxDNNNgb3AJ4AfU6xbL0mSJC2IjkmgRsS9gI+Xb9+WmR+vUS+AgzPzmhq3OnLSa0c1SJIkVeteFH2wZ2XmpxsdjCRJktrfokYHUIWIuAPwVeBQ4L+BV9aotwHYCrxkmtvdvzzvALbXMUxJktSEBkfHOP2s8zn1Pedw+lnnMzg61uiQOt0iigTq5xodiCRJkjpD2ydQI+IkYAC4OXAO8LRp1i3dQTHC9DkRsWSKe3UBzy/ffmkO659KkqQWNDg6xvqNWzj7wm0Mbd3J2RduY/3GLSZRG+u3FGuf3qzRgUiSJKkztHUCtdyh9WxgBTAIPCoz90zzkS8CFwO3AT4WEYdNutcJFBsW3BIYA/51oeKWJEnNoX9giD17x/cp27N3nP6B4QZFJOAjFAnUlzU6EEmSJHWGtk6gAi+lSJ4CrAa2RsTVNY77Zua1wJOAq4AnA5dHxI8j4pfAr4E7UWwi9dDM/G31P44kSarSyI7dNcp3VRyJJvkAxUPvF0XEPzU6GEmSJLW/dt9E6s6TXi9m+p93EUBmnhcRa4BXAY8E7kiRUD0X+BLw0czcuQCxSpKkJtPTtZShrTf+td/TtawB0aj0CODTwPHAWyLiicB/Ukztv3b/ypn5lWrDkyRJUrtp6wRqZj7wAD83Cjy3PCRJUofq613F5uHt+0zjX7J4EX29KxsYVcf7EsUmUlBM5b9TeUwlafP+riRJkhaeHUpJkqQa1nQvZ9OGtfQPDDOyYxc9Xcvo613Jmu7ljQ6tk41wQwJVkiRJWnAmUCVJkqaxpns5Z552SqPDUCkzj2t0DJIkSeos7b6JlCRJkiRJkiQdMEegSpIkqWVFxJ2AE8q3v8nMnzcyHkmSJLWfyhOoEXFzYNqtazNzpKJwJEmS1IIi4lHAu4Db7Fd+MfCSzPxKQwKTJElS26ksgRoRfw+8DlgxQ1V3S5UkSVJNEfE84H1MvRzVbYEvRsQ/ZObGaiOTJElSO6pkDdSyk/sB4CggZjhcl1WSJElTiog7Au+l6DN+HbgPcER53Af4GkWf8n0RsaZBYUqSJKmNVJWsfD5FR/YXFB3bQzNzUa2jopgkSZLUel5GMVvpi5n5iMzckpk7y2NLZj4S+GZZ52UNjVSSJEltoapk5fEUU/OfWnZs91TUriRJktrL/Sn6la+Yps4ry/MDFj4cSZIktbuq1hq9CrgF8H8VtSdJkqT2dCQwnpnD09T5FUWS9RbVhKS5GBwdo39giJEdu+npWkpf7yrWdC9vdFiSJEk1VTUC9TyKKfynVNSeJEmS2tMVwKKIOGGaOqsp+p7bqwlJszU4Osb6jVs4+8JtDG3dydkXbmP9xi0Mjo41OjRJkqSaqkqgvhnYC/x7RCytqE1JkiS1n++W53+dps6bKUag/r8Fj0Zz0j8wxJ694/uU7dk7Tv/AdAOKJUma3uDoGKefdT6nvuccTj/rfB/Mqe4qSaBm5o+ApwInAd+NiDtV0a4kSZLazjuAceCxEfGFiLhHRCwrj3tExOeBhwPXAe9saKS6kZEdu2uU76o4EklSu3B2g6pQyRqoEfGF8uWlFNP4fxIRvwZ+C1y7X/XMzMdWEZckSZJaS2b+PCJeArwXeFR5TOUlmfnzygLTrPR0LWVo684pypc1IBpJUjuYbnbDmae5kqTqo6pNpB5NMY0qyvcB3L489pcVxSRJkqQWlJnvi4g/UIxGvfV+ly8FXpaZm6qPTDPp613F5uHt+/xDd8niRfT1rmxgVJKkVubsBlWhqgTqGypqR5IkSR2gTJBuioi7AMeXxRdn5k8aGJZmsKZ7OZs2rKV/YJiRHbvo6VpGX+9K1nQvb3RokqQW5ewGVaGSBGpmmkCVJElS3ZUJU5OmLWRN93KnVEqS6sbZDapCVSNQJUmSJEmSpLpydoOqUHkCNSLuCzwGOKEs+g3wxcw8t+pYJEmSJEmS1Nqc3aCFVlkCNSKWAp/mhp1SJzaUSuAFEfEl4GmZOfXqv5IkSZIkSZJUsUoSqBERwBeBdRSJ098AvygvrwFWAo8GPg88pIqYJEmSJEmSJGkmiypq5+nAA4Grgcdn5qrMXF8etwMeB+wBTo2Ip1UUkyRJkiRJkiRNq6op/M+kmKr/yszctP/FzPx8RJwAvAV4NvCpiuKSJOmADY6O0T8wxMiO3fR0LaWvd5WL1Ut1EhGvLV9uz8wPTFE+K5n5xroGJkmSpI5TVQL1zuX549PU+ThFAvXO09SRJKkpDI6OsX7jFvbsHQdgaOtONg9vZ9OGtSZRpfp4PcUD+F8DH5iifCZR1jOBKkmSpHmpKoF6MEUHdroNonaVdQ6uJCJJkuahf2Do+uTphD17x+kfGHYHUKk+PknRN/xjjfKGi4hFwKLM3NvoWCRJkrRwqkqg/gY4CXgQ8I0adR7EDRtMSZLU1EZ2TP1McGTHroojkdpTZj5jLuVViYh7AK8E/gboKssuBj4BvCMzr65TOw8HvjpDtadl5qfr0Z4kSZJqq2oTqc9TJEf/LSK6979YlvVTjCa40RqpkiQ1m56upTXKl1UciaSqRMRpwPeBRwE3AX4IXAwcT7FUwEBELKlTc/csz3spNlud6riuTm1JkiRpGlUlUN8DXArcGvhlRHwwIv6hPD4I/BK4VVnnvRXFJEnSAevrXcWSxfv+Gl2yeBF9vSsbFJGkhRQRdwI+DBwEvAtYkZn3Ak4Anl9WWwu8rE5N3qM8Pykzb1Lj+Gyd2pIkSdI0KpnCn5l/KachfRPoBs7Yr0oAo8DDM/MvVcQkSdJ8rOlezqYNa+kfGGZkxy56upbR17vSDaSk9vV24BDg7Zn58onCzEzg3yPigRQjU58EvGk+DUVEAHcr314wn3tJkiRp/qpaA5XM/L+IWAO8GHgMxVQnKKY9fRF4T2ZeVVU8kiTN15ru5W4YJXWAiFhGsRnq+RRT9adyPkUC9Zg6NHkisBzYmpmX1OF+kiRJmofKEqgAmTkGvK48JEmSpKaXmbsoBgBMZyJxWo+d5O5Vns+tw70kSZI0T1WtgSpJkiS1pYg4iGL0KcBAHW45kUA9IiK+FRGXR8TVEXFxRHw4Ik6sQxuSJEmaJROokiRJaisR8byI+ElE/FNFTT6LYkNUqM+GqGvL84MokqkXAz8CuoDnAD+LiKccyI0jonu6AziqDvFLkiS1lUqn8EuSJEnzERG/A/6amdONwvwB8G/AEorNnxYynqOBt5ZvP56ZP53n/W4B3K58+zbgzRObrEbE4cC7gdOBj0bE5swcmWMTl80nPkmSpE5kAlWSJEmt5NbA1TPUGSzPt1nIQCJiEfBJipGhl1Jsljpfu4F1wOLM/N/JFzLzLxFxBnAHipGpLwBeWoc2JUmSNA0TqJIkSWormbk3Iq4DDlrgpv6FItm5B3hCZl413xtm5m7gO9Ncz4j4KEUC9cHMPYF67AzXjwLOn+M9JUmS2poJVEmSJLWViLgzRfJ0rtPb59LGE4B/Lt8+NzN/tFBtTWG0PB8/1w9m5uh01yPigAKSJElqZ3VNoEbExeXL32TmqVOUz0Zm5m3rGZckSZJaT0TcF7jfFJcWR8Rra3xsOfBEIIEvLFBc9wI+Xr59W2Z+fLr6B3D/g4DIzL01qhxZnrOe7UqSJGlq9R6Belx53n9dquOYPTuCkiRJArg/8Dpu3D88qCyvJYBfAG+sd0ARcQfgq8ChwH8Dr6zz/d8A9AHPBT5bo9r9y/NQPduWJEnS1OqdQJ3ozO2uUS5JkiTN1s+As/YrOw0YBz5d4zN/BX4MfDoz99QzmIg4CTgbuDlwDvC0zKz3w/9dFKNon88UCdSIOAF4cvl2QUbYSpIkaV91TaBm5jlzKZckSZJqycwvA1+eXBYRpwHXZuYzq4wlIlZQJE9XAIPAo+qdoC19lGJU69qIeAfw6ol2IuKuwOeBmwCXAe9fgPYlqekMjo7RPzDEyI7d9HQtpa93FWu6lzc6LEkdZFGjA5AkSZJawEspkqcAq4GtEXF1jeO+Ex+aVPbq2TSSmVcCTwP2lG1eHhHnR8SvgQuAWwO/Ak7NzKvq9+NJUnMaHB1j/cYtnH3hNoa27uTsC7exfuMWBkfHGh2apA5iAlWSJEma2Z0nvV4MLJnmmNzHniib9cyvzPwacDLwGeBa4E4U0/r/B3g2cHJmXnSAP4cktZT+gSH27B3fp2zP3nH6B4YbFJGkTlTvNVCbXkQsAhZNs6upJEmSmtczgeuqbjQzH3iAn4sD/NyFwFMP5LOS1E5Gduy/xcpE+a6KI5HUyTpiBGpE3CMivhQRV1J0uK+NiN9GxGsi4iY1PnN4RLy1rLcnIi6JiDdExJJqo5ckSdKEzDwrM2ttICVJajM9XUtrlC+rOBJJnaztE6jlRgPfBx5FseD+D4GLgeOBNwID+ydFI+Jm5WdeXtY7mGK9qdcC34iIjhu5K0mSJElS1fp6V7Fk8b6piyWLF9HXu7JBEUnqRE2TQI2I7oi4Y0QcWcd73gn4MHAQ8C5gRWbeCzgBeH5ZbS3wsv0++l/AGuCvwLMoEq/HAt8GHgC8pF4xSpIkSZKkqa3pXs6mDWtZt3oFq1YcxrrVK9i0YS1rupc3OjRJHaSSkZTlTqTjmfm9aao9DPgAsAl4Qp2afjtwCPD2zHz5RGFmJvDvEfFAipGpTwLeVMb6MGBijavnZOZnytejEfE44HfA6yPizHKXVEmSJEmStEDWdC/nzNNOaXQYkjpYVSNQvwt8a4Y6XwUC+Jt6NBgRy4DdwPkUU/Wncn55PmZS2YbyPDgpeQpAZo4BZ1KMSH1oPeKUJEmSJEmS1LyqXMtz2h1IM/MPETEO3KIejWXmLuAxM1SbSJxO3r5vIoH7uRqf+QbFlP+HAJ864AAlSWqQwdEx+geGGNmxm56upfT1rnIanCRJkiTV0DSbIZUbOQWwp6L2DqKYvg8wUJYdCUz8C/IHNT76i/J8+4WLTpKkhTE4Osb6jVvYs3ccgKGtO9k8vN21xNR0IuI75ctLM/OZU5TPRmZmb30jkyRJUqdpmgQq8A8UCdRfzFSxTp4F3Kp8/d7yfPNJ14em+lBm7oiIvcBxc2ksIrpnqHLUXO4nSdKB6B8Yuj55OmHP3nH6B4ZdW0zN5n7l+aIa5bORdYlEkqQm4UwiqTHqnkCNiD6gb4pLSyLi4hofOwK4GUUn9/31jml/EXE08Nby7ccz86fl6yWTqv1pmltcBdwiIpZk5mxHzF42tyglSaq/kR27a5TvmrJcaqCJUadjNcolSeooziSSGmchRqDelKlHZ0aN8glXA2/MzP+sf0iTgohYBHwS6AIuBV486fJ1k15P/S/MwjXl+VAqWnJAkqR66OlaytDWnVOUL2tANFJtmXnWXMolSWp3ziSSGmchEqifAL476X0A36FIOj6oxmf+CvxfufHTQvsXYB1F4vMJmXnVfnEA7M3M8f0/OMnEtaUUo1Fn49gZrh8FnD/Le0mSdED6elexeXj7Pp3vJYsX0de7soFRSZIkaSbOJJIap+4J1My8lGJk5/UiAmA8M8+pd3tzERFPAP65fPvczPzRflUmpu0vjogVmbm1xq1uNnHL2badmaMzxDbbW0mSdMDWdC9n04a19A8MM7JjFz1dy+jrXem0L0mSpCbnTCKpcZppE6kFFRH3Aj5evn1bZn58/zrlBlG7gGXAbYAbJVAjYll5HeDG/+eSJKnJrele7jQvSZKkFuNMIqlxOiKBGhF3AL5KsWbpfwOvnKb6j4H7AqcAP5zi+j3L858zc/9NDSRJkiRJDeQu5WpX7TiTyL+vahWVJFAzc1EV7UwlIk4CzgZuDpwDPC0zc5qPfJsigfpE4N+muP7A8vyTesYpSZIkSZofdylXu2unmUT+fVUraVhiswoRsYIieboCGAQelZl7ZvjYpyg2ibp3RDxiv/sdCTynfPu1OocrSdKNDI6OcfpZ53Pqe87h9LPOZ3DUyQ+SJNUy3S7lkpqLf1/VSto6gQq8lCJ5CrAa2BoRV9c47gvXb4L1n+Vn/iMinhIRh0bEXYFvAV3AVdywnqokSQti4qn82RduY2jrTs6+cBvrN24xiaqOFxFHR8R7IuJZk8puFRFfi4ixiPh1RDyukTFKagx3KZdah39f1UraPYF650mvFwNLpjkm/1k8H/gZcBjwaWA3cAFwMsXo1DMyc8fChi5J6nQ+lZduLCK6gPOAFwC3L8sC+F/gocDhwErgMxFx5waFKalBerqW1ih3l3Kp2fj3Va2krROomfnAzIxZHt+d9Lk/AfcB3g1MfvRxEfDQzPzvan8SSVIn8qm8NKWXALcCfgF8uSx7IMVso/8F/o4iwbqYIskqqYP09a5iyeJ9/5nrLuVSc/Lvq1pJJZtItaLM3AW8JCJeSzG64c+Z6ZAfSVJlerqWMrR15xTlPpVXR3sEkMDTM3OwLHsgcDXwhMwci4jfAT+leCAuqYO04y7lUrvy76taSV0TqBHRU768NjP/OEX5rGTmSD3jmo8ykfrjRschSeo8fb2r2Dy8fZ9p/D6Vl7g1xZJKv5xUdjJwXmZOLBA8WNa5VcWxSWoC7bRLuZrL4OgY/QNDjOzYTU/XUvp6V5nsmyf/vqpV1HsE6u/K80XAHaYon43EkbGSJPlUXprawRTLUC0Gri3LTgQmL7G0qDyy2tAkSe1qYnPPiQfbQ1t3snl4O5s2rLVvJnWAeicqozzvv7Zq7F9xFveQJKnj+VReupFLgVXAvYBzI+IOwFHsOyL1pPLcNLOaJEmtbbrNPe2rSe2vrgnUzJxyU6pa5ZIkSdIcfQu4HfAfEfGfwKMpRpqeDRARK4A3lWX/r0ExSpLajJt7Sp3NxKYkSZJayVuAbcAxwIuA44HPZubEklEXAg+h2FTqPQ2JUJLUdnq6ltYod3NPqROYQJUkSVLLyMytwFrgLODLwAuAp0+qsgj4DfDIzPxN9RFKktpRX+8qlizeN4Xi5p5S53CzJkmSJLWUzLwYeGaNy8dn5o4q45EktT8395Q6mwlUSZIktQ2Tp5KkhdIpm3sOjo7RPzDEyI7d9HQtpa93lYlidTwTqJIkSZIkSWJwdIz1G7ewZ+84AENbd7J5eDubNqw1iaqO5hqokiRJajlReHpEfDEiBsvjixHxtIiwjytJ0gHoHxi6Pnk6Yc/ecfoHhhsUkdQcKh2BGhEBLM3MXZPKbgXcHdiamVuqjEeSJEmtJyKOAr4C3HWiqDzfAXgk8PyIeGS54ZQkSZqlkR27a5TvmrJc6hSVPZ2PiGcA24DXTip7KjAMbAI2R8SXHDEgSZKkWiLiEOBrwClAAgPAu8vj7LLsFOBrEXFwo+KUJKkV9XQtrVG+rOJIpOZSSbIyIu4OfBS4OeUIgYi4OfAh4CbAX8ryRwBPqiImSZIktaQNwF2AHcC9M/PUzHxpeZwKrAXGyjobGhinJEktp693FUsW75sqWrJ4EX29KxsUkdQcqhrt+fLy/E5uGIH6aOBQ4LmZeVPgrRRJ1GdXFJMkSZJaz1MoRpm+MDN/tP/FzDyPou8ZwFMrjk2SpJa2pns5mzasZd3qFaxacRjrVq9wAymJ6tZAPbk8vz0zry5f3we4PDM/VL5/N/AK4HYVxSRJkqTWcyJFAvW/p6mziWKm04mVRCRJUhtZ072cM087pdFhSE2lqhGoxwDjmbl9UtlJwA8m3pTXxoFbVBSTJEmS2lM2OgBJkiS1j6oSqLuBRRFxM4CIWAysBn45USEijijj+UtFMUmSJHWEwdExTj/rfE59zzmcftb5DI6ONTqk+fgVxfT8x05TZ+LahQsfjiRJktpdVVP4fwncm2Ih/7cAz6RY//Qnk+qsK8+/qigmSZKktjc4Osb6jVvYs3ccgKGtO9k8vL2V1zP7LHA3oD8iLsrMyf1JIuJk4O0Uo1A/04D4JEnqOIOjY/QPDDGyYzc9XUvp613Vqv0MaUpVjUD9FMVIgTdFxHZgI/Bn4NsAEfEi4N8pOrpfqCgmSZKkttc/MHR98nTCnr3j9A8MNyiiefsA8HPg5sCPIuKbEfGO8vgmcB5ws7LOBxsYpyRJHWHiYe3ZF25jaOtOzr5wG+s3bmn1GS/SPqpKoJ5JsZh/AF0UU/rPyMzd5fXXAiuAn2FHV5IkqW5GduyuUb6r4kjqIzOvAR5G0W88CHgQ8OLyeFBZ9jPg4WVdSZK0gNrwYa10I5VM4c/MBB4fEX8LLAd+lJlbJ1X5X4qO7vsyc08VMUmSJHWCnq6lDG3dOUX5sgZEUx+Z+YeIuBvwdOAxwPHlpYuBLwKfysy9jYpPkqRO0m4Pa6WpVLUGKgCZeU6N8idUGYckSVKn6Otdxebh7fuMDFmyeBF9vSsbGNX8ZeZ1wMfLQ5IkNUg7PqyV9ldpAlWSJKmZteMGCGu6l7Npw1r6B4YZ2bGLnq5l9PWubPmfS5IkNYd2fVgrTdaQBGpE3IxJU60y80+NiEOSJGlCG+5Wf7013cs587RTGh1G3UXE8cBa4GiKzUh/D3wvMy9raGCSJHUQH9aqE1SaQI2IU4C3A/el2FAKICPiHODlmXlBlfFIkiRNmG4DhHZMPrayiDiSYpPSh09xOSNiE/BcH9JLklSNdn1YK01YVFVDEfEYYDPwt2W7UR6LgPsDmyPiUVXFI0mSNJkbILSGiDgM+C5F8nSiP7kT+Cs39C0fB3wnIg5tUJiSJElqI5UkUCPiNsCngCXAz4CnAncsj6cCPymvfToijqsiJkmSpMl6upbWKHcDhCbzMmA1RcL0FcCRmbk8Mw8DjgHeAFxD0c98acOilCRJUtuoagTqPwFLgXOBe2Xmf2TmL8vjP4B7AeeVdV5WUUySJEnX6+tdxZLF+3aN3AChKT2eYr3T52Xm2zPzyokLmXl5Zr4B+EeK0ahPalCMkiRJaiNVJVAfRNHRfVFmXrP/xcy8FngRRUf3IRXFJEmSdL2JDRDWrV7BqhWHsW71irbYQKoNHVeePzdNnc+U59ssbCiSJEnqBFVtInUMMJ6ZP52mzgUUSdZjqglJkiRpX26A0BL+DHQBe6epsxcYB9xESpIkSfNW1QjUMWBRRNxqmjo9FCNQx6oJSZIkSS3o2xR92MdMU+fxZZ2vVhKRJEmS2lpVCdQt5fmV09T55/L8/QWORZIkSa3r5cDvgQ9HxFMnX4iIRRFxBvBB4FdlXUmSJGleqprC/17gUcBzI+Ja4G2ZeTlARBxFscnUMymm8L+3opgkSZLUej4CXAF0A2dFxLuBYeAgYCVw07LeVcB/RMTkz2ZmPqyySCVJktQWKkmgZuY5EfEu4CXAC4AXRMSO8nLXpKrvysxzq4hJkiRJLenBFA/doVj+6Rblsb+1U5TlFGWSJEnStKoagUpmviwiLgdeCxwO3HzS5b8Ab8zMd1UVjyRJklrSGxodgCRJkjpLZQlUgMx8V0R8BFgHHF8WXwycnZl/rjIWSZIktZ7MNIEqSZKa0uDoGP0DQ4zs2E1P11L6elexpnt5o8NSHVSaQAUoE6VfqLpdSZIktb6IOAh4FnAycJMZqmdmPnvho5IkSZ1ucHSM9Ru3sGfvOABDW3eyeXg7mzasNYnaBipPoEqSJEnz8CGKzUehWAN1OgmYQJUkSQuuf2Do+uTphD17x+kfGObM005pUFSql0oTqBFxK+AM4N7A0RSd2t8D5wIfycxtVcYjSZKklvOY8nwu8H3g2gbGIkmSBMDIjt01yndVHIkWQmUJ1Ij4O+CTwKHsO1rgRIo1UV8WEU/JzK9XFZMkSZJazrUUD+EflJnXNDoYSZIkgJ6upQxt3TlF+bIGRKN6W1RFIxFxR+A/gKXAXuDrwPuBDwD/C4wDRwCfi4gTq4hJkiRJLenfKPqwj2t0IJIkSRP6elexZPG+abYlixfR17uyQRGpnqoagfrPwCHA+cBjM3N08sWIOB74PHAn4FXAUyqKS5IkSa3lzRR9xk9ExIOBHwA3Hu5RysxPVhWYJEnqXGu6l7Npw1r6B4YZ2bGLnq5l9PWudAOpNlFVAvX+FFOtNuyfPAXIzIsj4gzgRxTT+RdMRHwQ2ADcJjMvqVHn4cBXZ7jV0zLz03UOT5IkSdN7PvBYir7lk8tjOiZQJUlSJdZ0L3fDqDZVVQL1CGA8M39aq0Jmnh8R48DhCxVERLycInk6k3uW573AdTXq1CqXJEnSwnkNRfL0L8AvcRMpNZHB0TH6B4YY2bGbnq6l9PWucuSRJEltoKoE6qXAyog4MjOvmKpCRKygWM/qNwsRQES8GHjLLKvfozw/KTM3LUQ8kiRJOiCLKBKox2fmjkYHI00YHB1j/cYt7Nk7DsDQ1p1sHt7Opg1rTaJKktTiKtlECvgoEMAbpqkzkdx8fz0bjoilEfFZ4F3AjZYPmKJ+AHcr315Qz1gkSZI0b2dR9CuPa3AcAETEooioalCCmlj/wND1ydMJe/aO0z8w3KCIJElSvVSVQP0MxZqifx8RZ0fEwyNiZUTcPiL+LiK+D5wGnAl8OSJuOfmYZ9uvB55Isb7q3WdR/0RgObC11hqpkiRJapgPAOcBX4mI0yJiVUQcU+tYiAAi4h4R8aWIuJJiWadrI+K3EfGaiLhJndtaEREfjIjLImJPRFwUES+MiKr68ZqlkR27a5TvqjgSSZJUb1U9Lb9s0uv7l8dUnl0ekyXzi3OcYq2st2bm3mKA6bTuVZ7PnUebkiRJWhhDk15/bIa68+1H3khEnEYxu+ogYDfwQ+CWwPHAG4EHR8QDMnNPHdq6NfA9oLssSuB2wHuAkykGIKhJ9HQtZWjrzinKlzUgGkmSVE9VPbmOeRzzjfE1mfmmzNw7y/oTCdQjIuJbEXF5RFwdERdHxIcj4sR5xiNJkqQDV2U/ct+GI+4EfJgiefouYEVm3gs4AXh+WW0t8LI6tHUw8A2K5OmVwKOBg4HbAz8Dnh4RT5hvO6qfvt5VLFm8739ySxYvoq93ZYMikiRJ9VLVCNTbVNTOjWTmXHdmXVueH8S+u7veCXgO8IyIeGZmfmYuN42I7hmqHDXHOCVJkjpRw/qVwNuBQ4C3Z+bLJwozM4F/j4gHAo8CngS8aZ5tnUGxtFQCj8nMzWX5ryPiscCvgfdGxKbMvG6ebakO1nQvZ9OGtfQPDDOyYxc9Xcvo613pBlKSJLWBShKomXlpFe3MV0TcgmJaFMDbgDdn5l/Ka4cD7wZOBz4aEZszc2QOt79s5iqSJEmazv79yoi4GcX0eYCLM/NPC9FuRCyjmLJ/PsVU/amcT5FArcfaqxvK89cmJU8ByMyLI+KLwOMoHv5v3v/Daow13cs587RTGh2GJEmqs0qm8EfEmoj4fkTsjojrZjhmO9V+IewG1gEPysxXTCRPAcrXZwA/AJYAL2hMiJIkSYqIUyLiO8AVFJtKnQdcEREDEVH3DFZm7srMx2Tm3TOz1q5AE4nTee0aVCaFTyrffq5GtW+U54fMpy1JkiTNrKop/B8B7j7LujPu8rRQMnM38J1prmdEfJRindQHAy+dw+2PneH6URSjFiRJkjSNiHgM8B8U0+kn9x2DYrPSzRHxxMz8coUxHUQx+hRgYJ63O2HS6x/UqPOL8nz7udzYZaUkSZLmrqoE6hqK9Zv+HvguxZqirWq0PB8/ba39ZObodNcjGpY3liRJahkRcRvgUxQzgn5KsZnTRDLxjsCLgbsAn46INZl5SUWhPQu4Vfn6vfO8183L87XAJTXqbCvPx83x3i4rJUmSNEdVJVAvotiE6XOZ+eeK2jwg5eiByMxaSwkcWZ6zopAkSZJ0g38ClgLnAqdm5jWTrv0yIv6bYk3QuwEvA/5hoQOKiKOBt5ZvP56ZP53nLZeU5z9Ps0HUxFqvR8+zLUmSJM2gkjVQgRcC1wBnRkTTbkMZEW8ArqRYkL+W+5fnoYWPSJIkSft5EMWD7BftlzwFIDOvBV5EMZ1/wdcHjYhFwCeBLuBSihGw8zWRNN09TZ2Jn/3QOd772BmOu83xfpIkSW2vqhGoP6dYC+qxwIMj4iJgZ426mZm9FcW1v13AcuD5wGf3vxgRJwBPLt9+ocK4JEmSVDgGGJ9hlOcFFEnWY6apUy//QrEJ6R7gCZl5VR3u+dfyfKME8STj5XnpXG7sslKSJElzV1UC9dPAQylGAhwGTLczaiOnxn8UeCWwNiLeAbw6M/cARMRdgc8DN6FYO+r9DYtSkiSpc40Bt4iIW2Xm72vU6aHod44tZCAR8QTgn8u3z83MH9Xp1hPT86dLAN9sIow6tSlJkqQaqkqgPqA8f4piTaqm3EQqM6+MiKcBm4CXAqdHxG+AI4BVZbVfAY+t0+gCSZIkzc0W4JEUD72fX6PORFLz+wsVRETcC/h4+fZtmfnx6erP0Uh5PjQijsrMy6eos6I815rVJUmSpDqpKoH6O2A18Jyp1qpqJpn5tYg4GXgVcCrF5lc7gP8B/hv4dLP/DJIkSW3svcCjgOdGxLUUycvLASLiKIpNpp5JMavpvQsRQETcAfgqxfqj/02RzK2bzNweESMUI2lPAb42RbV7lueRKa5JkiSpjqraROo1FNOLFnwX1JlkZpTHJdPUuTAzn5qZt8zMQzLzqMx8SGZ+zOSpJElS42TmOcC7KPqWLwB+HxFXRMQVwO+BvrLquzLz3Hq3HxEnUaztf3PgHOBpmbkQS1B9uzw/scb1B5bnnyxA25IkSZqkqhGoJwLnAe+MiEdRTL2qOd0oM/+1orgkSZLUYjLzZRFxOfBa4HCKZOaEvwBvzMx31bvdiFgBnE0xfX4QeNTEevkL4BPAs4EnRMS7M/P6RGlE3B54TPl2qtGpkiRJqqOqEqhvophGFcB9gb+Zob4J1AUyODpG/8AQIzt209O1lL7eVazpXt7osCRJkuYkM98VER8B1gHHl8UXA2dn5p8XqNmXcsPao6uBrdPsWn/qxAjYiLi6LHtTZr5pNg1l5vci4vvAvYGvR8SzKUa+rgU+BhwC/Bb4yoH8IJIkSZq9qhKo51IkUNVAg6NjrN+4hT17xwEY2rqTzcPb2bRhrUlUSZLUcspE6RcqbPLOk14vZvq+9OSlspZM+sxcPIWiH90DfH2/a1cDp2VmU27OKkmS1E4qSaBm5v2qaEfT6x8Yuj55OmHP3nH6B4Y587RTGhSVJKnVObtBnSIzHzhzrSk/V3OY6gyfuzQiTgHeDjyZYtQpFEtjPT8zzz+Q+0qSJGluqhqBer2IuA3wSOCEsmgY+Gpm/q7qWDrNyI7dNcp3VRyJJKldOLtBjRIR64DHAy+dmLIfEcPAn4D3Z+anGhlfvWTmFcAzI+KFwErgisy8tLFRSaqKDyklqTksmrlKfUTEooh4N/Br4N3A88rjPcCvI+JdEVFZPJ2op2tpjfJlFUciSWoX081ukBZKRLwZ+BbFJks3mXTpJsApwCci4h2NiG2hZOZYZl5g8lTqHBMPKc++cBtDW3dy9oXbWL9xC4OjY40OTZI6TpUJy08AfRSjXncDPy2P3WXZCykWxNcC6etdxZLF+37lSxYvoq93ZYMikiS1Omc3qGoRcSrwSorNSS8B9ky6/EaKXekDeHFEPKDyACWpTnxIKUnNo5IEakQ8EngqMA78E9CVmadk5ilAF/Ayik2mnhYRj6gipk60pns5mzasZd3qFaxacRjrVq9wiqUkaV6c3aAG+EeKfuMHM/O2mXn9UKzM/EhmPhJ4H0US9fkNilGS5s2HlJLUPKpaA/W5FB3dt2TmOydfKHcOfVdEHAW8pKz71Yri6jhrupe7YZQkqW76elexeXj7PiNknN2gBXb38vyWaeq8C3gBsHbhw5Fam2tsNq+erqUMbd05RbkPKSWpalVN4Z/o6PZPU2fi2j0WOBZJklQnzm5QA9yU4sH876ep8/uyzs2qCEhqVa6x2dxcgk2SmkdVI1CXAuOZeeU0dbZTdHSnngsoSZKakrMbVLErgRXA7YELa9S5PcUU/un6nlLHm26NTf+/3ngTDyn7B4YZ2bGLnq5l9PWu9CGlJDVAVQnUS4GVEfE3mbm5Rp37UHR03VlUkiRJtZwLPI5iw6jH1ajzeooH8+dUFJPUklxjs/n5kFKSmkNVU/gndkN9X0Qcvv/FsqyfoqP7lYpikiRJUut5J0Wf8e8i4jMRcUpELC2Pu0bEp4H1ZZ33NDRSqcm5EaAkSbNTVQL17RRTqO4I/CIiXh4RDyuPlwM/B1aXdd45zX0kSZLUwTLzAuA1FA/nnwj8CPhLeZwHPKms+rrMPK8hQUotwjU2JUmanUqm8Gfmtoh4LPBl4NbAv+5XJYA/A4/NzG1VxCRJkqTWlJlviYg/AG8Gjtnv8h+BV2XmJyoPTGoxrrHZfgZHx+gfGGJkx256upbS17vK71OS6qCqNVDJzHMj4i4Ua1I9Epj4v/gYxbT9N2TmxVXFI0mSpNaVmWdFxKeAuwC3KYt/B/wkM8drf1LSZK6x2T4GR8dYv3HL9RuDDW3dyebh7WzasNYkqiTNU2UJVIDM/B1wWkQEcPOy+MrMzCrjkCRJUusrE6UXlIckdbT+gaHrk6cT9uwdp39g2CS5JM1TpQnUCWXCdHsj2pYkSZIkqd2M7Nhdo3xXxZFIUvupahOpmiLi4RFxh0bHIUmSpNYREWsj4qMR8fOI+G1Zdo+IuCQiXlLOeJKkjtHTtbRG+bKKI5Gk9lNZAjUiuiLiMxGxOyJuOenSB4FfRMTZEbGiqngkSZLUmiLijcBm4BnAGuDoSZd7gLcDZ1UfmSQ1Tl/vKpYs3vef+EsWL6Kvd2WDIpKk9lFJAjUibgJ8B3gSsGT/y+Vxf+DbEbH/dUmSJAmAiHgI8GqK/uM5+13+LfCJ8vVTIuLR1UUmSY21pns5mzasZd3qFaxacRjrVq9wA6k2NTg6xulnnc+p7zmH0886n8HRsUaHJLW9qtZAfSFwR+AvwDuBqyZduwOwDvhA+fp5wHsqikuSJEmtpQ9I4K2Z+aqIuH7HlMzcDjwrIn4DvAnYAHypIVFKUgOs6V7uhlFtbnB0jPUbt1y/YdjQ1p1sHt5uslxaYFVN4X8cRUf3OZn5L5l5zcSFzBzLzM9TdIaDYpSqJEmSNJW7lef3TVNnY3m++wLHIklSpfoHhq5Pnk7Ys3ec/oHhBkUkdYaqEqiryvNXp6nz9fJ8+wWORZIkSa1rGTCemVunqfNniof3U++oIklSixrZsbtG+a6KI5E6S1VT+A+i6MROtxvqdWWdqmKSJElS6/kj0BMRR2Xm5TXqnEjR76x1XWpJg6Nj9A8MMbJjNz1dS+nrXeWUXanD9HQtZWjrzinKlzUgGqlzVDUC9bcUndjeaeqsK+tcXElEkiRJakXfLc9nTFPn9RQP5v/fQgcjVWVi3cOzL9zG0NadnH3hNtZv3OLmMVKH6etdxZLF+6ZylixeRF/vygZFJHWGqhKoX6JIjr43Im65/8WIOJJi46gEvlhRTJIkSWo976KYufSaiPjXsiwi4vYR8ciI+Brw6LLOuxsUo1R3rnsoCYqNwjZtWMu61StYteIw1q1e4QZSUgWqmi7/buCZwHHAhRHxWeBX5bXVFBtHdVFMybKjK0mSpCll5i8j4gXAvwMvp3gAfwjwf2WVKMtemJmDjYlSqj/XPZQ0YU33cs487ZRGhyF1lEoSqJn5p4h4JPBN4EjguftVCeAK4FGZ+acqYpIkSVJrysyNEfE74M3AXfa7/GPgNZn5P9VHJi0c1z2UJKlxqprCT2b+BLgD8HbgQuDq8riwLDspMy+oKh5JkiS1rsz8VmaeAhwF3KM8jsrMu5k8VTty3UNJkhqn0h3vM3M78IrykCQ1OXf7ldTsMnMbsK3RcUgLbWLdw/6BYUZ27KKnaxl9vSv9vSxJUgUqTaBKklrHxG6/ExtWDG3dyebh7S5SL6lhIuJg4ETgaOCIsngMuBz4ZWZe16jYpCq47qHqyQflkjR7lSdQy47vSUBXZg5ExJHAHTLzu1XHIkmqbbrdfv3Hm6QqRcTjgTOA+wAH16h2dUQMAP2ZOVBZcJLUgnxQLklzU9kaqBGxPCI+DFwFXAB8pbx0PDAQET+PiBOqikeSND13+5XUaBGxLCK+CXwWuD9wCMXmo1MdhwIPA/43Ij4QEdGYqCWp+U33oFySdGOVjECNiKXAZopNpCY6sxPnLF+fRNHhvVNm/qWKuCRJtbnbr6Qm8GngQeXrvwDfoHgQ/ztgJzAOHAb0AHcBHgHcDPh7iof2/1xtuJLUGnxQLklzU9UU/hdRJEi3Af8EfGLiQmaeFxH3Az4J3Bp4MfCGiuKSJNXQ17uKzcPb9xmd4G6/kqoSEfcFHkXxsP2zwPMz86oZPrMUeC9wOvCyiPhYZv5mgUOV1GJc+9MH5ZI0V1VN4X88Ref32Zn5yf0vZua5FB3dANZXFJMkaRoTu/2uW72CVSsOY93qFa6LJalKTynP383Mp86UPAXIzN2ZeQbwvxT93DMWMD5JLWhi7c+zL9zG0NadnH3hNtZv3MLg6FijQ6tUX+8qlizeNx3gg3JJqq2qEagTa5uePU2dcyiSrLdd+HAkSbPhbr+SGujeFH3Dtx7AZ98BnAo8oK4RSWp5bpJZmHhQ3j8wzMiOXfR0LaOvd6UPyiWphqoSqNdRdID3TlNnKcUI1OnqSJIkqTMcXZ63HMBnf1Cej6tPKJLahWt/3qCTH5S7jIOkuaoqgToEnAzcGfhxjTrrJtWVJKnp2NmWKnVT4NrMnHNWIzN3R8Q15T0k6Xqu/amJZRwmRiIPbd3J5uHtLlUlaVpVJVA/T7Ez6tsi4qH7X4yIY4F3UoxS/XxFMUmSNGt2tqXKBRAR8fQD/Pyi8h6SdD03yVSnLePgAACpPqpKoL4feDZwf+CiibYj4m3ASuBBwKHA78q6kiQ1lU7rbEtNYjHw8QP8bFA8nJek67n2pzppGQcHAEj1U0kCNTN3liNPvwEcXxYfBLyUG0YGXAw8LDNvPJ9CkqQG66TOttREHEEqqe46ee1PddYyDg4AkOqnqhGoZOZQRJwEnA48hBsW9b+EIrH60cy8uqp4JC0Mp4ioXXVSZ1tqErdpdACSpPbTScs4OABAqp/KEqgAZYL038qjISLig8AG4DaZeck09VYArwceDtySYnmBjcD7MnO81uekTuYUEbWzduxs+8BDzSwzL210DJKk9tNJyzg4AECqn0oTqI0WES+nSJ7OVO/WwPeA7rIogdsB7wFOBk5bqBilVuYUEbWzduts+8BDzSgiDgEenZmfq/N97wtcnJmj9byvJKk1dcoyDu04AEBqlAVNoEbEsUAvcCfgaOCI8tIYcDlwATCQmZcvZBxlLC8G3jKLegdTLCnQDVxJsfnV14ATgP8Enh4R38jM/1rAcKWW5BQRtbt26mz7wENN6p3AP0TEHTLzdfW4YUScCnwB+DVw13rcU5KkVtBuAwCkRlqQBGpE3BZ4P3AqMy/+vzciPge8MDOvXIBYlgIfBZ4IXAYcO8NHzgBOpBh1+pjM3FyW/zoiHkvR+X5vRGzKzOvqHa/UypwiIrUOH3ioSb0OuDvw6oi4PfCczPzzgd4sIvqAd1A8vH9OfUKUJKl1tNMAAKmRFtX7hhFxd+BnwIPK+0d57AK2An8E/jKp/GDgycCPyxGr9fZ6iuTpjyg65DOZmOL/tUnJUwAy82Lgi8BRwNo6xii1hb7eVSxZvO//VpwiIjWnnq6lNcp94KHGycw/AX8LfAZ4HPCLiHj0XO8TEasi4n8oll+6CLh7Zv6knrFKkqT5Gxwd4/SzzufU95zD6Wedz+DoWKNDkqZU1wRqRNyEYorUxL++PgM8BDgyM4/IzGMyszszbwrcFHgAcCYwTjEytK7rXZXGgdcA95lpqYCIuBlwUvm2VizfKM8PqU94UvuYmCKybvUKVq04jHWrV7ieourCjlX9+cBDzSoz92Tm04GnAYcBn4+I8yPi2RFxy1qfi4hDIuKBEfFZYBBYB7yPInn6u0qClyRJszaxJv/ZF25jaOtOzr5wG+s3brGvr6ZU7yn8zwCOAfZQTH//n1oVy+lY3wW+GxGfoUhM3j0iHpmZX6ljTK/JzGtnWfeESa9/UKPOL8rz7ecSRER0z1DlqLncT2pWThFRvbnZ0cJwTSw1u8z8TER8HXgFxRJLHwHGI+ISYBj4E7AXOBw4jmLDzyUUD8+/BrwuM39x4ztLkqRm4Jr8aiX1TqA+hGLt0PdOlzzdX2aeExHvBl5FMd2+bgnUOSRPAW5enq8FLqlRZ1t5Pm6OoVw2x/qSJOxYLSQfeKjZZeZVwCsi4vXAwyhGld4JOBlYTtGX3QmMAl8FzgG+kpmjjYhXkiTNnmvyq5XUO4F65/L8qQP47KcpEqj3qFs0c7ekPP95mg2i/lSej64gHknqeHasJGXm1cDny0OSJLUBNyFWK6n3JlITIzh/fQCf/Q3F6NWaa1tVYCJpOvW/1gvXlOdD53jvY2c47jbH+0lSR3CzI0mSJKn9uCa/Wkm9R6AuBa6eZvRmTZl5XURcU96jUf5anq+Zps7EPNI5xTnTVLKImMvtJKlj9PWuYvPw9n2m8bdrx2pwdIz+gSFGduymp2spfb2rXJNUkiRJbck1+dVK6p1ABTgkIr5zoJ+tayRzNzE9/5hp6tysPJvxlKQKdErHys2yJEmS1Glck1+tYiESqAHc7wA+l+Vns67RzM1IeT40Io7KzMunqLOiPN94oQ5J0oLohI6Vm2VJkiRJUnOqdwL1XBqbAJ2XzNweESNAD3AK8LUpqt2zPI9McU2SpAPiZlmSJEmS1JzqmkDNzPvV834N8m3g2cATmTqB+sDy/JPKIpIktT13IZUkSZKk5rRo5iod5xPl+QkRcZfJFyLi9sBjyrdTJVclSTog7kIqSZIkSc3JBOp+MvN7wPcpRud+PSIeGhFLIuL+wDcpNrr6LfCVBoYpSWozE5tlrVu9glUrDmPd6hVuICXVEBEPjogtEfGXiLhummNvo2OVJElS61uITaTawVMo1nPtAb6+37WrgdMy89rKo5IktbUD2SxrcHSM/oEhRnbspqdrKX29q0y6qq1FxOOA/5x4O1P1BQ5HkiRJHaDjEqiZOWNHOjMvjYhTgLcDT6YYdQpwHvD8zDx/AUOUJGlWBkfHWL9xC3v2jgMwtHUnm4e3O3JV7e4VFInR0fL1jykecEuSJEkLouMSqLOVmVcAz4yIFwIrgSsy89LGRiVJ0g36B4auT55O2LN3nP6B4TmPZJVayIlAAk/MzC2NDkaSJEntzwTqDDJzDLig0XFIkrS/kR27a5TvqjgSqVK7gIOBHzQ6EEmSJHUGN5GSJKlF9XQtrVG+rOJIpEr9nGIK/+0bHYgkSZI6gwlUSZJaVF/vKpYs3vdX+ZLFi+jrXdmgiKRKvL08vzsi7MtKkiRpwdnplCSpRa3pXs6mDWtZt3oFq1YcxrrVK9xASm0vM78FvAQ4FfhSRKxocEiSJElqc66BKklSC1vTvdwNo9RRIuLd5ctB4GHA7yLiu8BvgWv3q56Z+ZIKw5MkSVIbMoEqSZKkVvJCIMvXAdwEePCkMiZdS4rRqgsiIj4IbABuk5mX1PnezwfeP0O1v8nM79WzXUmSJN2YCVRJkiS1kk9y42Rp5SLi5RTJ04Vyz/J8LTBeo06tckmSJNWRCVRJkiS1jMx8RqNjiIgXA29Z4GbuUZ7XZuYFC9yWJEmSpuEmUpIkSdIsRMTSiPgs8C5gdAHbuTlwAnANxVqvkiRJaiBHoEqSpLYwODpG/8AQIzt209O1lL7eVazpXt7osNReXg88EfgR8GjgjwvUzsT0/Z9m5p4FakOSJEmzZAJVkiS1vMHRMdZv3MKevcWSkENbd7J5eDubNqw1iap6GgdeA7w1M/dGxEK1c6/yfO5CNSCpvnyIJ0ntzSn8kiSp5fUPDF2fPJ2wZ+84/QPDDYpIbeo1mfmmzNy7wO1MJFCPj4hzI2J7RPw1In4dEe+JiGMXuH1JczDxEO/sC7cxtHUnZ1+4jfUbtzA4Otbo0CRJdeIIVEmS1PJGduyuUb6r4kjUzjLz2oVuIyIOAu5evn0ssAP4JUW//WTghcBzIuIRmfn/DuD+3TNUOWqu95Q63XQP8c487ZQGRdUZHPkrqSomUNU2/OUpSZ2rp2spQ1t3TlG+rAHRSPNyZ+AwiuUCXgp8YGId1Ig4EvgY8HDgvyLitpn5lzne/7I6xioJH+I1isv3SKqSCVS1BX95SlJn6+tdxebh7fuMAFqyeBF9vSsbGJXmIyIursNtMjNvW4f7VOli4FTgr5n5vckXMvOKiHgsMATcGjgN+LfqQ5Q0mQ/xGsORv5KqZAJVbcFfnpLU2dZ0L2fThrX0DwwzsmMXPV3L6Otd6UO01nZcHe6RdbhHpTLzT8C3p7l+TUR8Cng18GDmnkCdaf3Uo4Dz53hPqaP5EK8xHPkrqUomUNUW/OUpSVrTvdyHZu3l/o0OoImNlufj5/rBzByd7npEHFBAUifzIV5jOPJXUpVMoKot+MtTkqT2kpnnNDqGRomIQ4BrM7PWCNojy3PLjbCV2pUP8arnyF9JVVrU6ACkeujrXcWSxfv+5+wvT0mS1Goi4kzgT8A9p6k2MTp3aOEjkqTmNDHyd93qFaxacRjrVq9wDwxJC8YRqGoLTpuRJElt4hpgKfCPwA/2vxgRf8MNCdQvVBiXJDUdR/5KqooJVLUNf3lKkqQ28D7gdOBJEXEe8P7MvA4gIk4FPgsE8LPytSSpgQZHx+gfGGJkx256upbS17vKgTxSG3IKvyRJkrQAIuLWEXF1eTxtNp/JzIsoRp+OA+8B/hgR50XExcC3gC7g+8BDM3PvQsUuSZrZ4OgY6zdu4ewLtzG0dSdnX7iN9Ru3MDg61ujQJNWZCVRJkiRpYQSwpDwOmu2HMvNDwL2BLwMHA3cq7/ElYD1w38z8Y72DlSTNTf/A0D6bWAHs2TtO/8BwgyKStFCcwi9JkiQdgMyMGa5fQpFEPZB7/xB49IF8VpJUjZEdu2uU76o4EkkLzRGokiRJkiRJc9TTtbRG+bKKI5G00ByBKklTcDF4SZIkSdPp613F5uHt+0zjX7J4EX29KxsYlaSFYAJVkvYzsRj8REdoaOtONg9vZ9OGtSZRJUmSJAGwpns5mzaspX9gmJEdu+jpWkZf70r/zSC1IROoUoM4wrF5TbcY/JmnndKgqCRJkiQ1mzXdy/03gtQBTKBKDeAIx+bmYvCSJEmSJGmCm0hJDTDdCEc1novBS5IkSZKkCY5AlRrAEY7N7UAWg3dJBkmSJEmS2pMJVKkBerqWMrR15xTljnBsBnNdDN4lGSRJkiRJal8mUKUGOJARjqrWXBaDd9MpSZIkSZLalwlUqQHmOsJRzc0lGTqLyzVIkiRJUmcxgSrNUr2TJnMZ4ajm5pIMncPlGiRJkiSp8yxqdABSK5hImpx94TaGtu7k7Au3sX7jFgZHxxodmppAX+8qlize93+nLsnQnqZbrkGSJEmS1J5MoEqzYNJE05lYkmHd6hWsWnEY61avcERim3K5BkmSJEnqPE7hl2bBpIlmMpclGVxDs3W5XIMkSZIkdR5HoEqz0NO1tEa5SRPNjctBtDaXa5AkSZKkzmMCVZoFkyb1Mzg6xulnnc+p7zmH0886v+MShy4H0dpcrkGSJEmSOo9T+KVZmEia9A8MM7JjFz1dy+jrXWnSZI7cwdzlIKD1lzCYy3INkiRJkqTWZwJVmiWTJvM33ejLTvmz7fQ1NDstid7qyWJJkiRJklP4pxQRR0TEdRGR0xxnNjpOqdU4+tLlIDppCQPXu5UkSZKk9mACdWp3p/izGQf21DiubVh0UotyMy7X0OykJHonJYslSZIkqZ05hX9q9yjPH8zM5zc0EqmN9PWuYvPw9n2SSp00+nJCJy8H0UlLGLRCstglBiRJkiRpZo5AndpEAvWChkYhtZlOH32pzlrCoNlHXLvEgCRJkiTNjiNQp3bP8vzDhkYhtaFOHn2pG5Lo/QPDjOzYRU/XMvp6V7ZlEr3ZR1y7qZskSZIkzY4J1P1ExAnAkcC2zLyo0fFIUrvplCR6syeLW2GJAUmSJElqBiZQb+xe5fnKiPgssBZYAfwJ+AHwkcz8ZqOCkyS1jmZOFnfSerSSJEmSNB+ugXpja8vzauBxwJXAZuA64DHANyLi3+d604jonu4AjqrXDyBJ0kw6aT1aSZIkSZoPR6De2H3K8zeB52bmpQARsQj4e+DfgOdFxLmZ+V9zuO9l9Q1TkqQD1+xLDEiSJElSszCBemNnAMcAX83MayYKM3Mc+GBE9ACvAF4GzCWBKklSU2nmJQYkSZIkqVmYQN1PZv5ghipnUiRQ7xoRt8jM7bO89bEzXD8KOH+W95IkSZIkSZJUAROoczc66fXxwKwSqJk5Ot31iJhPTJIkSZIkSZIWgAnUKUTEkszcU+PykZNeZxXxSJre4OgY/QNDjOzYTU/XUvp6V7mOoyRJkiRJqgsTqJNExKOA91NsIPX3NardvzyPA7+pIi5JtQ2OjrF+4xb27B0HYGjrTjYPb2fThrUmUReQSWtJkiRJUqdY1OgAmswfKdYqfUJEHLn/xYg4hGL9U4DvZuafqgxO0o31DwxdnzydsGfvOP0Dww2KqP1NJK3PvnAbQ1t3cvaF21i/cQuDo2ONDk2SJEmSpLozgTpJZp4HfA9YDvxnRNxi4lpErAC+AZwIXAu8uiFBStrHyI7dNcp3VRxJ52impPXg6Binn3U+p77nHE4/63yTuJIkSZKkunMK/42dBnwXeADw+4j4FRDAauAQ4CrgGZn5g0YFKOkGPV1LGdq6c4ryZQ2IpjM0S9La5RskSZIkSVVwBOp+MvNi4E7AO4DfAycBPcBPgNcAJ2TmlxsXoaTJ+npXsWTxvv8rW7J4EX29KxsUUfvr6Vpao7zapHUzjYSVJEmSJLUvR6BOoVzb9J/KQ1ITW9O9nE0b1tI/MMzIjl30dC2jr3elIxAXUF/vKjYPb98nedmIpHWzjISVJEmSJLU3E6iSWt6a7uWcedopjQ6jYzRL0trlGyRJkiRJVTCBKkmas2ZIWjfLSFhJkiRJUnszgSpJaknNMhJWkiRJktTeTKBKklpWM4yElSRJkiS1t0UzV5EkSZIkSZKkzmQCVZIkSZIkSZJqMIEqSZIkSZIkSTWYQJUkSZIkSZKkGkygSpIkSZIkSVINJlAlSZIkSZIkqYbFjQ5AkqROMjg6Rv/AECM7dtPTtZS+3lWs6V7e6LAkSZLUIeyPSnNnAlWSpIoMjo6xfuMW9uwdB2Bo6042D29n04a1dlolSZK04OyPSgfGKfySJFWkf2Do+s7qhD17x+kfGG5QRJIkSeok9kelA2MCVZKkiozs2F2jfFfFkUiSJKkT2R+VDowJVEmSKtLTtbRG+bKKI5EkSVInsj8qHRgTqJIkVaSvdxVLFu/7q3fJ4kX09a5sUESSJEnqJPZHpQPjJlKSJFVkTfdyNm1YS//AMCM7dtHTtYy+3pUu2C9JkqRK2B+VDowJVEmSKrSmezlnnnZKo8OQJElSh7I/Ks2dU/glSZIkSZIkqQYTqJIkSZIkSZJUgwlUSZIkSZIkSarBBKokSZIkSZIk1WACVZIkSZIkSZJqMIEqSZIkSZIkSTWYQJUkSZIkSZKkGkygSpIkSZIkSVINJlAlSZIkSZIkqQYTqJIkSZIkSZJUgwlUSZIkSZIkSarBBKokSZIkSZIk1WACVZIkSZIkSZJqWNzoAHS9gyZe/PGPf2xkHJIkSQdkvz7MQbXqqanZJ5UkSS1rofqjkZn1upfmISJOAc5vdBySJEl1crfMvKDRQWhu7JNKkqQ2Urf+qFP4JUmSJEmSJKkGR6A2iYhYAqwp314BXLdATR3FDaMK7gZcvkDtaG78XpqX301z8ntpXn43zamq7+Ug4Mjy9WBm7lmgdrRAKuqT+v+J5uV305z8XpqX301z8ntpXlV8NwvSH3UN1CZRfqELPs0tIia/vTwzRxe6Tc3M76V5+d00J7+X5uV305wq/l4uXcB7a4FV0Sf1/xPNy++mOfm9NC+/m+bk99K8Kvxu6t4fdQq/JEmSJEmSJNVgAlWSJEmSJEmSajCBKkmSJEmSJEk1mECVJEmSJEmSpBpMoEqSJEmSJElSDSZQJUmSJEmSJKkGE6iSJEmSJEmSVENkZqNjkCRJkiRJkqSm5AhUSZIkSZIkSarBBKokSZIkSZIk1WACVZIkSZIkSZJqMIEqSZIkSZIkSTWYQJUkSZIkSZKkGkygSpIkSZIkSVINJlAlSZIkSZIkqQYTqJIkSZIkSZJUgwlUSZIkSZIkSarBBKrUBiLikIiIRschSZKkzmR/VJLUzkygtqCIODgiXhERv4qIPRHxh4joj4jlC9DWbSPiUxGxNSKujoifRcTT6t1Ou6jqu4mIgyLieRHxk4j4K7AHuDoivhsRD6pnW+2gyr8zNdp/X0RkRDyjivZaSaO+m4hYFRG7yu/lOQvZViuq8P9lE+38rPw+dkXEUER8JiJOrmdb7SYiPlj+93vcAt3f3/+akX3S5mR/tDnZH21e9kebk/3R5tdx/dHM9GihA1gCfBvI8hif9HoQOKKObZ0MjNVo6w2N/rNotqOq7wY4BPjGpHtfBnwfuGpSu//Y6D+PZjmq/DtTo/1HTmrvGY3+82imo1HfDXAQ8MOynW82+s+h2Y4K/192E+D/1WgngeuAMxr959GMB/DySX9Oxy3A/f397zHjYZ+0OQ/7o8152B9t3sP+aHMe9keb/+jE/qgjUFvP+4F1FP/x/BNwOHAL4NPAScDb6tFIRNwM+CZwBPA74G+BxcDdgUuBV0fEverRVhup5LsB/gV4CHAF8OjMPDYz7w3chuKXTADvjIjj69Req6vqe7mRiOgGPr5Q928DjfpuXgncg+IfeacvUButrKrv5VXA/YBfAPek6MAeSvF75usUs2Te7//L9hURLwbesoD39/e/Zss+aXOyP9qc7I82L/ujzcn+aBPr2P5oo7PWHrM/gDUUT0ASeNV+1w4GLqL4H8xJdWjrHWU7u4GV+137m/LaeY3+M2mWo6rvBrg1cA3FFKk7TXH9FsC1ZRwva/SfS6OPKv/OTNH2QcA57Pu07BmN/jNplqNR3w3Fk8xrynaf2ug/h2Y7Kv4987uynbtMcW0xsLW8/txG/7k0wwEsBT5b/pmMsEBP/P397zGbwz5pcx72R5vzsD/avIf90eY87I8279Hp/VFHoLaWMyiegGyn+A/qepl5LfA+iqe9j5lPIxGxmBuegp2ZmcP7tbUZOB+4W0T0zKetNlLJdwOsBrYA78nMn+9/MTO3A5eUb4+ZZ1vtoKrvZSqvBe4L/Ab40gLcv9VV/t1ExBLgUxQdry9m5qfrde82UuX3cqvyPLz/hczcC/yxfHtoHdpqB68Hngj8iOLpe935+19zYJ+0OdkfbU72R5uX/dHmZH+0eb2eDu6PmkBtLfctz1/OzGumuP6N8vyQebazBrhp+fpzNepMtPXgebbVLir5bjLzfzLzfpn5iqmul/+zObJ8u2s+bbWJqv7O7CMi/hZ4NcXIjMcDf67n/dtEI76bNwN3oOiMbajjfdtJld/L78vzQ/e/UE6TOrF8++M6tNUOxoHXAPfJzMsXqA1//2u27JM2J/ujzcn+aPOyP9qc7I82r47ujy6usjHN2wnl+QdTXczMSyLiz8Dt69TOdRSZ/an8ojzPt612UdV3M5N1wPLy9cACt9UKKv9eIuLmwGcoHlC9KDN/GhH1un07qfS7iYj7AC8q3/4P8I/l2jpbga9m5s/q0U4bqPJ7+TDwr8AHIuJg4FsUUz7vDvRTjMzYDJxbh7bawWvKURcLyd//mi37pM3J/mhzsj/avOyPNif7o82ro/ujjkBtERFxE4r1JgCGpql6BXCziDhiHs3dvDxfmpl7atTZVp6Pm0c7baHi72a6OILiKTPAz4DvLkQ7raKB38vHKaaC/FdmfrBO92wrVX83ZWfoQ9zwO68XuBtwZ4q/Mz+NiLMiYtl82ml1Dfg781aKaUCHU0xl2wb8iaLjejuKTQIenuViR52ugs4q+Ptfs2CftDnZH21O9kebl/3R5mR/tLl1en/UBGrrWDLp9Z+mqTdx7eg6tLXQ7bSLKr+b6ZwB3Lt8/UL/J1/99xIRfcAjKNaZes5879fGqv5unssN029eBRybmQ/OzPsAxwJfAZ4OfDk6e3hG1d/LIRQ7ax40xbWJ6Vq3mGcbmht//2s27JM2J/ujzcn+aPOyP9qc7I+qaX/3m0BtHddNer17mnoTf8nns8jxRFsL3U67qPK7mVJErAbeXb7tz8xz6t1GC6r0e4mIk4G3A1cDj8vMv8znfm2u6r8zLynP38rMf83M69vPzG0UC6H/gWIkwMPm2VYrq/p7+QDwYoq+yDXAeRTTdHZTdJyeClwQEbebZzuaPX//azbskzYn+6PNyf5o87I/2pzsj6ppf/ebQG0df530eqqFlCeMl+el09SZbVsL3U67qPK7uZGIOBz4Qnnf84B/quf9W1hl38v/b+/egywp6zOOPw+7wCJ3EFFAdrhJCSpqQIlGGEgBSUAuKTQrgm6ImhSgIt4gEFkFQgQRLblJKJiEwkAgIDcVQd1VAkHBGDUgXtjloojuwkq47C67+8sf79s1Te/pM2fmnDmnd+b7qXqrT3e/3e87/fY5/Zu3b7Y3knSN0hnMj/D8ojH1s212llS8nfHLrfJExPOSvp5HD51oWVNAP9tlD0nH5tEbJW0bEW+OiDdJmi2puN1wc0mfnmg5GDeO/+gEMWkzEY82E/FocxGPNhPxKBp77KcDdS2Rz1AVZxC3bZN18zzs5rL/4nLoyS5nSuhz27yI7XUkfUXp4cmPSzqi5k2F006f2+UiSbtIujoiLuliPdNCn9tmq9Lne9rkeyIPt2+TZ0rrc7scnoe/kDQnIhaX6rE4Io5T+idQ4s3a/cTxH2MiJm0m4tFmIh5tLuLRZiIehRp87KcDde3ySB7u0CbP1nn4TA/KGWrz/JVelDOV9Kttqj4v6RBJz0p6e0T8pofrngr61S7H5OEc21FNkt6b519Rmjbd9attni59Xtom38Z5+GwXZU0F/WqXIiC6LSKW1eS5Ng83tT3db83tF47/6BQxaTMRjzYT8WhzEY82E/Ho9NbYYz8dqGuX+/Jwz1Yzbe+k0QccP9IqT4f+V9JySbMk7V6TZ+8elDOV9Kttyus8WdKHlS5fPyoi7htjkemoX+2yfIxU3GKwsjRtuutX2/xK6TlgUroypk7xm/ZAF2VNBf1ql+Ifg3ZXKBX/2C3Pt7Vh8nH8R6eISZuJeLSZiEebi3i0mYhHp7fGHvvpQF273J6HR9pu9Za4A/JwUUS0e2NZW/mH4c48OqcmW1HWDydazhTTl7Yp2P6YpLPz6PERcVO365yi+vWdmdUuSboyZ31/adp016+2WSbp23n0lFZ5bP+5pL3y6M0TLWuK6Ndv2f15+Lo2eYbz8K4uysE4cPzHOBCTNhPxaDMRjzYX8WgzEY9OY00+9tOBuna5Qeny/9mSjivPsD1L6e1xknRLD8oaycMTbG9XKWt/SW/uYVlTQd/axvZcSefm0TN4xlFb/fzOYHz62TafUrrq4kjbl9reMZezhe0PSrou57stIto9l2o66Fe7XK/0fKth22+pzszTPpBHz+uyLIzPSB5y/Ec7xKTNRDzaTMSjzUU82kzEoxjJw2Yd+yOCtBYlSWcpXUb+gtLtMhtL2lXSHaXpryrln610u8AySceMo5z1JT2U1/kzSW9VuoT6CElL8vQFg94eTUr9aBulkx6/zeuL0vKt0oOD3iZNSP36zoxRh5Fc1txBb48mpX62jaT35OWK787K0ueQ9HNJWw16mzQh9fE483alW6eWKb185DRJn1EKhFblss4b9PZoairtu0M18zn+kyY19fG3gn2yYe0i4tFGtksHdRgR8ehA20bEo41rFxGPdttO0yoeHfgGJ42zwdKOdFvlh7acPlrJP1SaN3ecZb1R0pM15Swp/2CR+tM2Sg+6rlt/NS0a9DZpQurnd6ZNHUZ6ub6pkvrdNkpvpv2spP/Jv20rlJ5Jdb6kLQa9PZqS+nyc2VnSl5SedfSMUjD8hKSvKb3FeeDbo6mptM2HauZz/CdNaurzbwX7ZIPaRcSjjWyXDuow0sv1TZXU77YR8Wjj2kXEo92007SKR50rhrWI7ZmSPql06foWefJjkk6OiKt6XNYOSm/WPFSjj3y4XdJxEfHLXpY1FfSzbdA52qW5aJtmol3A8R+dICZtJn7Dm4l2aS7apploFzTt2E8H6lrM9vqSXq101uqBmMTGtL2V0tmDxyLi8ckqZ6roZ9ugc7RLc9E2zUS7gOM/OkFM2kz8hjcT7dJctE0z0S5oyrGfDlQAAAAAAAAAqLHO2FkAAAAAAAAAYHqiAxUAAAAAAAAAatCBCgAAAAAAAAA16EAFAAAAAAAAgBp0oAIAAAAAAABADTpQAQAAAAAAAKAGHagAAAAAAAAAUIMOVAAAAAAAAACoQQcqAAAAAAAAANSgAxUAAAAAAAAAatCBCgAAAAAAAAA16EAFAAAAAAAAgBp0oAIYONsjtsP2vEHXBf1he25u8/mDrgsAAADx6PRDPApgPGYOugIA0E+loPgLEbF0gFVpDNvDkoYl/SgivjrIugAAAEx1xKNrIh4F0HRcgQqgCR6X9KCkxX0o6/ScNutDWWuLYaVtcvhgqwEAADAwxKODNSziUQANxhWoAAYuIk6RdMqg6wEAAIDpiXgUANAOV6ACAAAAAAAAQA06UAEAAAAAAACgBh2owDRXeuPoZbb3sX2P7edsf8/2drZ3t70gT/up7f1arGMn25fYXmh7ue3f2b7e9j7jrMO8DvMfafubtn9ve4XtR21/w/ZBLfLOz+sO21GatbA8vVq27aEWy3Rcb9uL8rzhPL6j7bNt/9j2U7Zn16xzfdvH2/5P20ttL7P9oO1zbG/ZyfYZi+3hyjY5Pc96b2WbtPvbt7b9uVy3ZbafzG1wWC/q2KK8Idtfsv3zXN4fbH/X9tFtlnnRm1Vtr2N7ju2bbT9i+9I2y+5r+0bbD+V9eont220fWpO/2Gavz214qu0H8vfmSdvX2d6xZlnbfqftO3Le5bYftn2F7Ve3qeM6tt+dl1uSvwuLbF9se/vajQkAQMOYeJR4lHi01bLEo0CTRASJRJrGSdKIpJD0Q0nLJa3O4yHpO5J+XxoPSb+pLP8OSc+V5q+q5D97HHWY10Hecyvrr5Y3r5L/m5KWlVKRb3ll+mmV5YaKvBOpt6RFed6wpGMkPVOp51CLZbaWdG8pz+pKezzaarkJtPk+lb99ZWlblqcva7P84jZtcKWkmWPUYW7OO7+D+h5W2X5rlDdWGZJeKulbleVGapY7u5JvZWX8Qy2WKea9VdKdlTYsPi+U9JLKcutLuqGy/vIyz0s6tEV5L5F0W5vlnpK05yB/W0gkEolE6jSJeJR4lHi0uhzxKInUsMQVqAAKb5B0saSNJZ2Vpw0rvY10G0kH5mmvsP1ySbL9x5KukrSBpAWS/kjp5XTbSyrOpp5s+8ReVND2DpI+lkcvyvWaKWlzSR/N00+zvVWxTEQcGBGzilRa3a7l6RFxZi/q2MJBSoHtQklzJG2nFJg+Vs5k25L+Q2kb/kTS/krbdZakfSX9Mi97WbcViojvVrZJ8bdfWdkms6rL5rPWN0naMtdzP0nrSnqZ0n6zWtLRkj7XbT1zea+VdLWkDZX2z52V2nxLSScpBa9Htzvzn/Nfq7Qdz5f0RqUA9qQW5e0t6eQ8erGkbSKi2MfOy9PPsr1BTVkXSnqzpFMlbSFpE0mfyPOGJL2nkv8SpbfNrlTaftsrbc83SPqeUvtfafulleX+Wek7+UhefkNJ60naU9I9Sm/1vco2x3kAwNqEeHRyEI92gXiUeBSQpIH34JJIpMEmjZ61fkjSjDztAI2eOXxVnrZuadpQnrYgj98rab0W674wz/+DpE06qMO8Meq6R6kOa6xP0hlKgdL2bdZRe8a9km+oyDuRemv0jP9qSXdI2mCM8g4pbauXtZi/b6nus3u8D8xTmzPglbz/kvM+LGmzFvM/rtGz5Du1Wc9cdXDGX9J1Od/VNfOvyPO/06aM1ZJekHRwB3/fHElfVbpqYZ3KvBm5fULSXjX7VUg6qsV6b8/zLixN202jZ+n/rsUy25fWeXRp+mtK23j3mn23WO++vdxXSCQSiUSajCTi0bp8Q0XeidRbxKPEoy+eTzxKInWROBMAoPD9iFiVP7+Qh0si4ueSFBEvlDPnM5Bvy6PnRcSKFuv8xzzcRNKf9qCOD0h6PH/+aPWsa0T8Q0R8LCIe6UFZvbJE0pyIeH6MfH+Rh5tIeqLFs5/ml/LuOQn1HFM+e3xYHr0kIpa2yPZFSc8qBXctn880zvKK54j9VXWb5O0yN89vt00s6cyIuHWsMiPi6og4PCKOiYjVuR4b52eHfV7pihgpnWFv5aaI+EqL6fe1WO7gXLen1eJKjrwfv1zSKyRdX5pV7CszJP20xTZZmNcrDWhfAQBggohHJwfxaHflEY8SjwKaOegKAGiMZzqcVthBowfFn7bKEBG/tv2k0q0jO3dXPSkiVth+l6RrJH1K6XasB5Vu3fkvSTdExGPt1tFjnfyGnh0RizvIN5SHxdnpdjbvYH2TYUtJm+bPdW2+IrfJG9V9m79U0kb58wtK26bOurbXj4jlLeY9IemcTgu1vamk9ypd+bKHpFfmWcXZd9csKkn/VjN9WYtpO+ThLyJiZauFIuKJFpOHSvVp9Y9i2WZjzAcAoEmIR8ePeLSCeJR4FJgMdKACWKtExAKnN4buL2kvSbsrPSj9KEnn2z41Ij7bp+p08mbJH45znddHxDsmUpkp7ohOztjX+FkHV1xIkmy/SdLNSs/Q+rXSc58elPTj/PkHkma3WcWvJljHibgvIvbqY3kAAEDEo9MY8eiaiEcxbdCBCmCiFmr07OdrlM66v4jtbZXO9kvpofM9kc/qfj2noqzDlR7Mfrbt+RFxT6/Ksz2jdDtZMW1rpYez98qiPNylTT3eoPSWzF9HxKM9LLtTS5SeubSpUpvfXM1gez1Ju+bRbtt8sdJVJxupZrvY3kTp2U2S9KOIaHVmfTz+VSlYvVrpOU/Vdh9r+bGu1ihbmIe72J7Z6qy/7SuUrjyYHxHFiwkW5eFOth0R0WK53ZRuv1scET377gEA0DDEo8SjxKNrIh4FJgHPQAUwIfk2oDvz6Edsr9siW/H2yKclfavbMm2fa3up7S+2qM9XJd2vFED/SZvVFAHNpm3ySNJzpc+7tph/ntJbJnvla3m4h+01AmHbQ0ovR7hbKYDppeJseNttkp/BdFMe/dt8a1HVB5Weq7SqlHdCcnm35dG/rtnHTlTaJvPV/lamMeV/Qoq2vqZFsHqU2p/tH69blV9AIenYFvXZSNKfSdpW6bavQvGP2uaS1rg6JC93d07Vt6wCADBlEI8SjxKPdo14FOgQHagAuvFJpTOce0laYHtv2+vZ3sX2iKQTcr7TI+LpHpT330pB1d/YPja/OEC2N7X9YaWz0NKLD+5VxcPT/972Fnn5zWwfbnu/IlNE/E7S7/LoOcULAmxvaPtSSe/W6AsEeuFWpQBDkm6x/T7bW9meaXtvpTdxrqP0t13Xw3Kl0W1ykO39bM/I6dW2P1HJ+2mlf0BmS7rb9gG2N7D9StvnavTZThdERC9uH/qMpOWSXifpW7bfYntWfpD+BySdmvNd3ultUW08q/QmUSkF5Fvn7f9625covQm10FVwLEkRcX9pnRfYPsP2trbXzVd33KD00P4Vkr5cWu4nSlckSNLltj9ue5vcZq+RdKNSEPy8pMu7rScAAA1HPNo7xKOtEY8SjwJSRJBIpGmcJI0onXW8rDRtOE9bVMlbPLR8qDTtHUpnx4t5q0ufQ9I/jaMO88bIN0PSNyrrX1UZv1fSrDbr2FcpACjyryx9PrGS94TSvCWS7lG6ZSgkndau3kq3tYSk4XG0xcuVgvLy31Penk9Kessk7AOubNfyNl1asw0Xt2nzKyXNHKPMuTnv/A7q95dKwWRdeXdI2rCbMkrLXNxm+/9A0qP587E1343X16x3Xp4/Upk+S+nWs7oyn5f0zhbr2yj/3e2WO6LX+wqJRCKRSJORRDxKPEo8Wl6GeJREamDiClQAXYmIayW9VtKlkh5RCgAXK52hHo6Ik+uXHndZqyQdLOl9kr4t6fdKB+nFSm89PUHSPtHmuUMRsUDplqpbJD2lFKwtVbrl5vuVvBdI+pCkn0naWOktnvdKOiQizuzV31Uq77eS9s5l3i3p/5S25y8lfUHSbhFx1ySUG5IOU3qT7INKAesKSQ8pBeXV/AuUrq44T9IvlK76WCrpm0pB0jFR8xbPCdbveqV97CKlh+K/oPQsqrskvV/SgRHxbI+KO15p/yr+OXlOqc1PkvQ2jV6VcZztGd0WlvfVQyW9S2mf/oNSmz8s6TKlAPjfWyz3jKQDld7O+h2l7b9KKaC+TNLrIuKGbusHAMDagHi0d4hHa+tHPLrmcsSjmFacfqcAAAAAAAAAAFVcgQoAAAAAAAAANehABQAAAAAAAIAadKACAAAAAAAAQA06UAEAAAAAAACgBh2oAAAAAAAAAFCDDlQAAAAAAAAAqEEHKgAAAAAAAADUoAMVAAAAAAAAAGrQgQoAAAAAAAAANehABQAAAAAAAIAadKACAAAAAAAAQA06UAEAAAAAAACgBh2oAAAAAAAAAFCDDlQAAAAAAAAAqEEHKgAAAAAAAADUoAMVAAAAAAAAAGrQgQoAAAAAAAAANehABQAAAAAAAIAadKACAAAAAAAAQA06UAEAAAAAAACgBh2oAAAAAAAAAFCDDlQAAAAAAAAAqPH/hQN0mIMT68QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = []\n",
    "for i in range(3):\n",
    "    ind = (i) * 3 + 1\n",
    "    y = Decomposition_Rate[ind][Decomposition_Rate[ind].size-34:Decomposition_Rate[ind].size]\n",
    "    k.append(y.tolist())\n",
    "\n",
    "y = np.array(k).T.mean(axis=1)\n",
    "moisturefilename = \"..\\data\\data.csv\"\n",
    "moistureData = pd.read_csv(moisturefilename)\n",
    "moisture = np.array(moistureData).T\n",
    "x = moisture[4]\n",
    "plt.rcParams['figure.figsize'] = (8.0,3.0)\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.ylabel(\"Decomposition rate\")\n",
    "plt.xlabel(\"moisture tolerance\")\n",
    "plt.scatter(x,y,s = 2)\n",
    "plt.subplot(1,2,2)\n",
    "y = np.log(y.tolist())\n",
    "plt.ylabel(\"ln(Decomposition rate)\")\n",
    "plt.xlabel(\"moisture tolerance\")\n",
    "plt.scatter(x,y,s = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.298231798 0.397206538 0.194219911 0.106552749 0.442035661 0.0\n",
      " 0.355601783 0.537592868 0.565364042 0.314249629 0.420505201 0.59667162\n",
      " 0.759123328 0.859955423 0.73089153 0.592213967 0.705631501 0.759123328\n",
      " 0.882362556 0.902942051 0.713863299 0.793803863 0.666300149 0.657845468\n",
      " 0.690074294 0.680594354 0.525572065 1.0 0.969821694 0.754680535\n",
      " 0.659465082 0.602421991 0.893566122 0.154130758]\n",
      "['10 °C' 4.07 3.2 2.94 3.78 2.35 2.03 2.29 2.18 1.72 3.56 2.83 10.41 4.29\n",
      " 5.64 2.67 2.29 3.47 2.16 22.78 13.52 11.18 6.4 5.15 2.3 2.61 3.68 2.22\n",
      " 11.88 5.97 4.44 3.92 2.55 5.35 2.17]\n",
      "[4.07 3.2 2.94 3.78 2.35 2.03 2.29 2.18 1.72 3.56 2.83 10.41 4.29 5.64\n",
      " 2.67 2.29 3.47 2.16 22.78 13.52 11.18 6.4 5.15 2.3 2.61 3.68 2.22 11.88\n",
      " 5.97 4.44 3.92 2.55 5.35 2.17]\n"
     ]
    }
   ],
   "source": [
    "moisturefilename = \"..\\data\\data.csv\"\n",
    "moistureData = pd.read_csv(moisturefilename)\n",
    "moisture = np.array(moistureData).T\n",
    "print(moisture[4])\n",
    "y = Decomposition_Rate[1][Decomposition_Rate[1].size-34:Decomposition_Rate[1].size]\n",
    "print(Decomposition_Rate[1])\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'D:\\\\python\\\\mcm_icm2021-a\\\\code'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2288\u001b[0m                 )\n\u001b[0;32m   2289\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_draw_disabled\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2290\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2292\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2801\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2803\u001b[1;33m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[0;32m   2804\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[0;32m   2805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3044\u001b[0m                 \u001b[0martists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3045\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3046\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_title_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3047\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3048\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxison\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_update_title_position\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2994\u001b[0m                 \u001b[0m_log\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'top of Axes not in the figure, so title not moved'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2995\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2996\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mymin\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mtop\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2997\u001b[0m                 \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2998\u001b[0m                 \u001b[0mtitle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[1;34m(self, renderer, dpi)\u001b[0m\n\u001b[0;32m    908\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    909\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 910\u001b[1;33m             \u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    911\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    912\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;31m# Full vertical extent of font, including ascenders and descenders:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m         _, lp_h, lp_d = renderer.get_text_width_height_descent(\n\u001b[0m\u001b[0;32m    310\u001b[0m             \u001b[1;34m\"lp\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m             ismath=\"TeX\" if self.get_usetex() else False)\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, s, prop, ismath)\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[0mflags\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_hinting_flag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m         \u001b[0mfont\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_agg_font\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    270\u001b[0m         \u001b[0mfont\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m         \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# width and height of unrotated string\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m_get_agg_font\u001b[1;34m(self, prop)\u001b[0m\n\u001b[0;32m    302\u001b[0m         \"\"\"\n\u001b[0;32m    303\u001b[0m         \u001b[0mfname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfindfont\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m         \u001b[0mfont\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_font\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    305\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[0mfont\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36mget_font\u001b[1;34m(filename, hinting_factor)\u001b[0m\n\u001b[0;32m   1422\u001b[0m         \u001b[0mhinting_factor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'text.hinting_factor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1423\u001b[0m     \u001b[1;31m# also key on the thread ID to prevent segfaults with multi-threading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1424\u001b[1;33m     return _get_font(filename, hinting_factor,\n\u001b[0m\u001b[0;32m   1425\u001b[0m                      \u001b[0m_kerning_factor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'text.kerning_factor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1426\u001b[0m                      thread_id=threading.get_ident())\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m_get_font\u001b[1;34m(filename, hinting_factor, _kerning_factor, thread_id)\u001b[0m\n\u001b[0;32m   1403\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mlru_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1404\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_font\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhinting_factor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_kerning_factor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthread_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1405\u001b[1;33m     return ft2font.FT2Font(\n\u001b[0m\u001b[0;32m   1406\u001b[0m         filename, hinting_factor, _kerning_factor=_kerning_factor)\n\u001b[0;32m   1407\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'D:\\\\python\\\\mcm_icm2021-a\\\\code'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "k = []\n",
    "for i in range(3):\n",
    "    ind = (i) * 3 + 1\n",
    "    y = Decomposition_Rate[ind][Decomposition_Rate[ind].size-34:Decomposition_Rate[ind].size]\n",
    "    k.append(y.tolist())\n",
    "\n",
    "x1 = np.array(k).T.mean(axis=1)\n",
    "\n",
    "k = []\n",
    "for i in range(3):\n",
    "    ind = (i) * 3 + 1\n",
    "    y = extension_Rate[ind][extension_Rate[ind].size-34:extension_Rate[ind].size]\n",
    "    k.append(y.tolist())\n",
    "\n",
    "x2 = np.array(k).T.mean(axis=1)\n",
    "X = np.stack((x1,x2)).T\n",
    "print(X.shape)\n",
    "# 2 肘部法求最佳K值\n",
    "K = range(1, 10)\n",
    "mean_distortions = []\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X)\n",
    "    mean_distortions.append(\n",
    "        sum(\n",
    "            np.min(\n",
    "                cdist(X, kmeans.cluster_centers_, metric='euclidean'), axis=1))\n",
    "        / X.shape[0])\n",
    "plt.plot(K, mean_distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "font = FontProperties(fname=r'', size=20)\n",
    "plt.ylabel(u'平均畸变程度', fontproperties=font)\n",
    "plt.title(u'用肘部法确定最佳的K值', fontproperties=font)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "cc5f70855ac006f3de45a3cc3b9e7d8d53845e50458809cb162b0174266dec97"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
